Welcome to Advanced Mathematics for PhD Candidates
Description: The aim of this course is to give the participants an idea of how the mathematical vocabulary is used and the strength of using it properly. This is needed by engineering PhD's when reading papers, which will often use the common language of mathematics, and in particular when writing papers. Moreover, it is crucial, that engineers understand when a given mathematical toolbox is applicable and when it is not. As an example: In favourable cases, differential equations have unique solutions, but this is not always true, and trying to approximate a solution in such cases may lead to results which are simply wrong. The topics covered are: metric spaces, convergence, continuity, compactness, completeness. Vector Spaces and linearity. Korovkins theorem on polynomial approximations via Bernstein polynomials. The Banach Fixed Point Theorem. Existence and uniqueness results for ordinary differential equations. The approach in the course is to stress the necessity of precise mathematical formulation, and, in particular, to give examples where the intuitive answer is not correct.
Prerequisites: A master's degree in engineering or similar.
Learning objectives: The approach in the course is to stress the necessity of precise mathematical formulation, and, in particular, to give examples where the intuitive answer is not correct.
Teaching methods: Lectures and exercises.
Criteria for assessment: Research delivery
Key literature: Cooper, T. (Ed.). (2010). Longer lasting products: Alternatives to the throwaway society. Gower Publishing, Ltd.
Bakker, C., Wang, F., Huisman, J., & Den Hollander, M. (2014). Products that go round: exploring product life extension through design. Journal of cleaner Production, 69, 10-16.
Haase, L. M., & Laursen, L. N. (2022). Designing for longevity: expert strategies for creating long-lasting products. Routledge.
Organizer: Morten Nielsen
Lecturers: Morten Nielsen
ECTS: 4.0
Time: 3, 5, 8, 12, 15, 17 December 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 30
Deadline: 12 November 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: The aim of this course is to give the participants an idea of how the mathematical vocabulary is used and the strength of using it properly. This is needed by engineering PhD's when reading papers, which will often use the common language of mathematics, and in particular when writing papers. Moreover, it is crucial, that engineers understand when a given mathematical toolbox is applicable and when it is not. As an example: In favourable cases, differential equations have unique solutions, but this is not always true, and trying to approximate a solution in such cases may lead to results which are simply wrong. The topics covered are: metric spaces, convergence, continuity, compactness, completeness. Vector Spaces and linearity. Korovkins theorem on polynomial approximations via Bernstein polynomials. The Banach Fixed Point Theorem. Existence and uniqueness results for ordinary differential equations. The approach in the course is to stress the necessity of precise mathematical formulation, and, in particular, to give examples where the intuitive answer is not correct.
Prerequisites: A master's degree in engineering or similar.
Learning objectives: The approach in the course is to stress the necessity of precise mathematical formulation, and, in particular, to give examples where the intuitive answer is not correct.
Teaching methods: Lectures and exercises.
Criteria for assessment: Research delivery
Key literature: Cooper, T. (Ed.). (2010). Longer lasting products: Alternatives to the throwaway society. Gower Publishing, Ltd.
Bakker, C., Wang, F., Huisman, J., & Den Hollander, M. (2014). Products that go round: exploring product life extension through design. Journal of cleaner Production, 69, 10-16.
Haase, L. M., & Laursen, L. N. (2022). Designing for longevity: expert strategies for creating long-lasting products. Routledge.
Organizer: Morten Nielsen
Lecturers: Morten Nielsen
ECTS: 4.0
Time: 3, 5, 8, 12, 15, 17 December 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 30
Deadline: 12 November 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Morten Nielsen
Welcome to Scientific Computing using Python - High Performance Computing in Python (2025)
Description
Many research projects involve scientific computing for analyzing [big] data and/or simulating complex systems. This makes it necessary have a systematic approach to obtaining well-tested and documented code. Further, we see an increased interest in reproducible research, which allows other researchers the opportunity to dig further into research results as well as easy access to results and improving productivity by reusing code and software.
This is a course in scientific computing using the popular programming language Python. Python is popular in science due to a number of advantages such as having a rich set of libraries for computing and data visualization, excellent performance-optimizing possibilities, standard tools for simple parallel computing, fast development cycle and high productivity – just to name a few. Python is open source and as such an asset for any researcher following the reproducible research paradigm.
This part of the course covers the main area: High performance computing.
High performance computing
High-performance computing and computer architectures
Performance optimization
Numba (just in time compilation)
Numba GPU functionality
Other options - superficially: Cython (compiled Python via C-extensions), f2py (inclusion of Fortran code in Python)
Parallel/distributed computing
Theoretical aspects (Amdahl's and Gustafson-Barsis' law)
Parallel computing on one computer
Distributed computing across multiple computers
Audience: The targeted audience is all PhD students with some experience in Python programming and an interest in developing high-performance Python code for various scientific computing purposes. By this we mean code to solve actual problems where [lots of] floating-point computations are needed.
Prerequisites: Participants must have some experience in programming Python. If not, there is an introductory course "Scientific Computing using Python - 1. Python + Scientific Computing". Further, some basic skills in general use of a computer are expected. The tools applied work best using Linux or Mac OSX – Microsoft Windows may experience challenges when using parallel computing.
Criteria for assessment: A pre-specified project must be delivered (4-8 pages description) in addition to the developed code. The code must include testing/validation, and performance evaluation of parallel computing. An acceptable project and at-least 75% participation is required to pass the course.
Learning objectives: After completing the course the participants will:
Know how to use methods and software for performance optimization.
know when and how to apply parallel computing for scientific computing.
Organizer and lecturer: Sokol Kosta
ECTS: 2.0
Time: 18, 19 June 2025
Place: Aalborg University TBA
Zip code: 9220
City: Aalborg
Number of seats: 25
Deadline: May 28, 2025
NB This course is only for PhD Students at Aalborg University
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description
Many research projects involve scientific computing for analyzing [big] data and/or simulating complex systems. This makes it necessary have a systematic approach to obtaining well-tested and documented code. Further, we see an increased interest in reproducible research, which allows other researchers the opportunity to dig further into research results as well as easy access to results and improving productivity by reusing code and software.
This is a course in scientific computing using the popular programming language Python. Python is popular in science due to a number of advantages such as having a rich set of libraries for computing and data visualization, excellent performance-optimizing possibilities, standard tools for simple parallel computing, fast development cycle and high productivity – just to name a few. Python is open source and as such an asset for any researcher following the reproducible research paradigm.
This part of the course covers the main area: High performance computing.
High performance computing
High-performance computing and computer architectures
Performance optimization
Numba (just in time compilation)
Numba GPU functionality
Other options - superficially: Cython (compiled Python via C-extensions), f2py (inclusion of Fortran code in Python)
Parallel/distributed computing
Theoretical aspects (Amdahl's and Gustafson-Barsis' law)
Parallel computing on one computer
Distributed computing across multiple computers
Audience: The targeted audience is all PhD students with some experience in Python programming and an interest in developing high-performance Python code for various scientific computing purposes. By this we mean code to solve actual problems where [lots of] floating-point computations are needed.
Prerequisites: Participants must have some experience in programming Python. If not, there is an introductory course "Scientific Computing using Python - 1. Python + Scientific Computing". Further, some basic skills in general use of a computer are expected. The tools applied work best using Linux or Mac OSX – Microsoft Windows may experience challenges when using parallel computing.
Criteria for assessment: A pre-specified project must be delivered (4-8 pages description) in addition to the developed code. The code must include testing/validation, and performance evaluation of parallel computing. An acceptable project and at-least 75% participation is required to pass the course.
Learning objectives: After completing the course the participants will:
Know how to use methods and software for performance optimization.
know when and how to apply parallel computing for scientific computing.
Organizer and lecturer: Sokol Kosta
ECTS: 2.0
Time: 18, 19 June 2025
Place: Aalborg University TBA
Zip code: 9220
City: Aalborg
Number of seats: 25
Deadline: May 28, 2025
NB This course is only for PhD Students at Aalborg University
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Sokol Kosta
Welcome to Scientific Computing using Python - 1. Python and Scientific Computing (2025)
Description: Many research projects involve scientific computing for analyzing [big] data and/or simulating complex systems. This makes it necessary to have a systematic approach to obtaining well-tested and documented code. Further, we see an increased interest in reproducible research, which allows other researchers the opportunity to dig further into others' research results as well as easy access to results and improved productivity by reusing code and software.
This is an introductory course in scientific computing using the popular programming language Python. Python is very popular in science due to a number of advantages such as having a rich set of libraries for computing and data visualization, excellent performance-optimizing possibilities, standard tools for simple parallel computing, fast development cycle, and high productivity - just to name a few. Python is open source and as such an asset for any researcher following the reproducible research paradigm.
The course covers two main areas:
1. The Python programming language itself and
2. various aspects of scientific computing.
This specific course content is as follows:
The Python language
1. Course introduction
1.1 Historical overview of scientific computing and high performance computing
2. Python development environment
2.1 Python from above
2.2 Data types, built-in functions
2.3 Branching and looping
2.4 Functions (definition, built-in, lambda)
2.5 Modules and packages
3. Debugging and testing
3.1 Pytest
3.2 Doctest
3.3 Pdb (breakpoints and post-mortem debugging)
4. Basic scientific computing packages
4.1 Pandas (tabular data - numerical computing)
4.2 Seaborn (data visualization) + other visualisation packages
4.3 H5py (data storage/access via HDF)
4.4 NumPy (numerical computing - array-based - vectorization) lighter overview
4.5 SciPy (various tools for integration, optimization, etc.) lighter overview
4.6 Documentation using Sphinx
Scientific computing:
1. Basic issues related to computational sciences such as
1.1 Floating-point representation
1.2 Numerical accuracy and condition number
1.3 Cancellation
1.4 Algorithmic complexity
2. Scientific software development
2.1 Version control (via git)
2.2 Code documentation
2.3 Test procedures (what to test - and how)
2.4 Code refactoring
Audience: The targeted audience is all PhD students with an interest in developing robust, portable and high-quality code for various scientific computing purposes. By this we mean code to solve actual problems where [lots of] floating-point computations are needed. It is not a course in object-oriented programming and we apply a procedural approach to programming in the course.
Prerequisites: Participants must have some basic experience in code development in e.g. MATLAB, C or FORTRAN. Further, some basic skills in general use of a computer are expected. The tools applied work best using Linux or Mac OSX - Microsoft Windows may experience challenges if using parallel computing (but this should not be a problem for this first part of the course).
Learning objectives: After completing the course the participants will:
1 have fundamental knowledge of important aspects of scientific computing
2. be able to map a mathematically formulated algorithm to Python code
3. know how to document, debug and test the developed code.
4. know when and how to optimize Python code
Teaching methods: A combination of lectures, demonstrating examples using Jupyter notebooks, smaller exercises, and a project is used to facilitate learning. The course is rich in examples and active user participation is expected - the topics covered demand a “learning by doing” approach.
Criteria for assessment: A pre-specified project must be delivered (4-8 pages) in addition to the developed code. The code must include testing/validation and performance evaluation. An acceptable project and at-least 75% participation is required to pass the course.
Key literature: We expect to use a combination of the following:
Selection of a few chapters in Python books (specified under the individual days below)
References to Python and all relevant packages (freely available via http://python.org)
A number of scientific papers relevant for specific parts of the course (specified under the individual days below)
Organizer and lecturer: Associate Professor Jimmy Jessen Nielsen
ECTS: 2,5
Time: 11, 12, 13 June 2025
Place: Aalborg University TBA
Zip code: 9220
City: Aalborg
Number of seats: 35
Deadline: 21 May 2025
NB This course is only for students at Aalborg University
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course..
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: Many research projects involve scientific computing for analyzing [big] data and/or simulating complex systems. This makes it necessary to have a systematic approach to obtaining well-tested and documented code. Further, we see an increased interest in reproducible research, which allows other researchers the opportunity to dig further into others' research results as well as easy access to results and improved productivity by reusing code and software.
This is an introductory course in scientific computing using the popular programming language Python. Python is very popular in science due to a number of advantages such as having a rich set of libraries for computing and data visualization, excellent performance-optimizing possibilities, standard tools for simple parallel computing, fast development cycle, and high productivity - just to name a few. Python is open source and as such an asset for any researcher following the reproducible research paradigm.
The course covers two main areas:
1. The Python programming language itself and
2. various aspects of scientific computing.
This specific course content is as follows:
The Python language
1. Course introduction
1.1 Historical overview of scientific computing and high performance computing
2. Python development environment
2.1 Python from above
2.2 Data types, built-in functions
2.3 Branching and looping
2.4 Functions (definition, built-in, lambda)
2.5 Modules and packages
3. Debugging and testing
3.1 Pytest
3.2 Doctest
3.3 Pdb (breakpoints and post-mortem debugging)
4. Basic scientific computing packages
4.1 Pandas (tabular data - numerical computing)
4.2 Seaborn (data visualization) + other visualisation packages
4.3 H5py (data storage/access via HDF)
4.4 NumPy (numerical computing - array-based - vectorization) lighter overview
4.5 SciPy (various tools for integration, optimization, etc.) lighter overview
4.6 Documentation using Sphinx
Scientific computing:
1. Basic issues related to computational sciences such as
1.1 Floating-point representation
1.2 Numerical accuracy and condition number
1.3 Cancellation
1.4 Algorithmic complexity
2. Scientific software development
2.1 Version control (via git)
2.2 Code documentation
2.3 Test procedures (what to test - and how)
2.4 Code refactoring
Audience: The targeted audience is all PhD students with an interest in developing robust, portable and high-quality code for various scientific computing purposes. By this we mean code to solve actual problems where [lots of] floating-point computations are needed. It is not a course in object-oriented programming and we apply a procedural approach to programming in the course.
Prerequisites: Participants must have some basic experience in code development in e.g. MATLAB, C or FORTRAN. Further, some basic skills in general use of a computer are expected. The tools applied work best using Linux or Mac OSX - Microsoft Windows may experience challenges if using parallel computing (but this should not be a problem for this first part of the course).
Learning objectives: After completing the course the participants will:
1 have fundamental knowledge of important aspects of scientific computing
2. be able to map a mathematically formulated algorithm to Python code
3. know how to document, debug and test the developed code.
4. know when and how to optimize Python code
Teaching methods: A combination of lectures, demonstrating examples using Jupyter notebooks, smaller exercises, and a project is used to facilitate learning. The course is rich in examples and active user participation is expected - the topics covered demand a “learning by doing” approach.
Criteria for assessment: A pre-specified project must be delivered (4-8 pages) in addition to the developed code. The code must include testing/validation and performance evaluation. An acceptable project and at-least 75% participation is required to pass the course.
Key literature: We expect to use a combination of the following:
Selection of a few chapters in Python books (specified under the individual days below)
References to Python and all relevant packages (freely available via http://python.org)
A number of scientific papers relevant for specific parts of the course (specified under the individual days below)
Organizer and lecturer: Associate Professor Jimmy Jessen Nielsen
ECTS: 2,5
Time: 11, 12, 13 June 2025
Place: Aalborg University TBA
Zip code: 9220
City: Aalborg
Number of seats: 35
Deadline: 21 May 2025
NB This course is only for students at Aalborg University
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course..
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Jimmy Jessen Nielsen
Welcome to Data Science using R
Description: In todays data-driven world, the ability to handle quantitative data systematically and reproducibly using reliable software is essential. This course, Data Science with R, is designed to introduce you to R, a powerful and free programming environment that has been a cornerstone of data analysis and statistical methodology for over two decades. R a versatile tool and is widely used in academia, industry, and government for a variety of data analysis tasks. This includes, for example, biostatistics, bioinformatics, analysis of data from life sciences, medicine, biology, engineering, physics and social sciences.
Data Science with R will equip you with the skills to tackle a variety of data challenges through a blend of practical programming and theoretical knowledge. The curriculum encompasses:
- Introduction to R: Discover R as a premier tool for statistical programming and data analysis.
- Efficient Data Management: Master techniques for managing and manipulating data efficiently.
- High-Level Graphics: Create stunning, informative visualizations with Rs advanced graphical capabilities.
- Statistical Modeling: Dive into theoretical and practical aspects of statistical modeling.
- Big Data Analytics: Learn how to extract insights from large datasets.
- Reproducible Research: Implement reproducible research practices to ensure your work can be trusted and verified.
- R Programming: Develop robust programming skills in R to solve complex data problems.
- Application areas: Examples include biostatistics and analyzing data from life sciences, medicine, social sciences, spatial data, physics, engineering, and more.
Prerequisites: A working knowledge of elementary statistical methods is required. This includes topics such as: summarizing data, estimation of e.g. mean and variance, hypothesis testing, confidence intervals, linear regression models, and analysis of variance. Such topics are covered in many courses, including the ?Applied Statistics? course offered at Aalborg University.
Learning objectives: Upon completing the course, participants will:
- Master R Programming: Confidently solve programming tasks using R.
- Data Management and Visualization: Effectively manage, visualize, and analyze data using R?s powerful tools.
- Model Fitting: Fit and interpret statistical models to extract meaningful insights from data.
- Independent Learning: Acquire sufficient knowledge to continue exploring and advancing their R programming skills independently. Competitive Edge: Be at the forefront of modern data science methods, enhancing your competitiveness in the field.
Teaching methods: The course employs a dynamic blend of instructional approaches, including: Instructive Videos: Engaging video tutorials to introduce and explain key concepts. Computer Practicals: Hands-on sessions to apply learning in real-time, practical scenarios. Lectures: Comprehensive lectures to provide theoretical foundations and contextual understanding.
Criteria for assessment: Active participation in the practicals + approval of major exercise (to be handed in after the course).
Key literature: R for data science by Wickham, Cetinkaya-Rundel and Grolemund
Organizer: Ege Rubak
Lecturers: Ege Rubak
ECTS: 4.0
Time: 5 - 6 February, 26 - 27 February and 12 - 13 March 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Number of seats: 50
Deadline: 15 January 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: In todays data-driven world, the ability to handle quantitative data systematically and reproducibly using reliable software is essential. This course, Data Science with R, is designed to introduce you to R, a powerful and free programming environment that has been a cornerstone of data analysis and statistical methodology for over two decades. R a versatile tool and is widely used in academia, industry, and government for a variety of data analysis tasks. This includes, for example, biostatistics, bioinformatics, analysis of data from life sciences, medicine, biology, engineering, physics and social sciences.
Data Science with R will equip you with the skills to tackle a variety of data challenges through a blend of practical programming and theoretical knowledge. The curriculum encompasses:
- Introduction to R: Discover R as a premier tool for statistical programming and data analysis.
- Efficient Data Management: Master techniques for managing and manipulating data efficiently.
- High-Level Graphics: Create stunning, informative visualizations with Rs advanced graphical capabilities.
- Statistical Modeling: Dive into theoretical and practical aspects of statistical modeling.
- Big Data Analytics: Learn how to extract insights from large datasets.
- Reproducible Research: Implement reproducible research practices to ensure your work can be trusted and verified.
- R Programming: Develop robust programming skills in R to solve complex data problems.
- Application areas: Examples include biostatistics and analyzing data from life sciences, medicine, social sciences, spatial data, physics, engineering, and more.
Prerequisites: A working knowledge of elementary statistical methods is required. This includes topics such as: summarizing data, estimation of e.g. mean and variance, hypothesis testing, confidence intervals, linear regression models, and analysis of variance. Such topics are covered in many courses, including the ?Applied Statistics? course offered at Aalborg University.
Learning objectives: Upon completing the course, participants will:
- Master R Programming: Confidently solve programming tasks using R.
- Data Management and Visualization: Effectively manage, visualize, and analyze data using R?s powerful tools.
- Model Fitting: Fit and interpret statistical models to extract meaningful insights from data.
- Independent Learning: Acquire sufficient knowledge to continue exploring and advancing their R programming skills independently. Competitive Edge: Be at the forefront of modern data science methods, enhancing your competitiveness in the field.
Teaching methods: The course employs a dynamic blend of instructional approaches, including: Instructive Videos: Engaging video tutorials to introduce and explain key concepts. Computer Practicals: Hands-on sessions to apply learning in real-time, practical scenarios. Lectures: Comprehensive lectures to provide theoretical foundations and contextual understanding.
Criteria for assessment: Active participation in the practicals + approval of major exercise (to be handed in after the course).
Key literature: R for data science by Wickham, Cetinkaya-Rundel and Grolemund
Organizer: Ege Rubak
Lecturers: Ege Rubak
ECTS: 4.0
Time: 5 - 6 February, 26 - 27 February and 12 - 13 March 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Number of seats: 50
Deadline: 15 January 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Ege Rubak
Welcome to Introduction to Probabilistic Machine Learning
Description: Machine learning (ML) and artificial intelligence have had major impacts on all areas of society and across research disciplines. Probabilistic ML provides a principled approach, based on probabilistic methods, to develop intelligent systems that make optimal decisions under uncertainty. Many problems in science can be casted as decision problems under uncertainty. In consequence, Probabilistic ML allows the application of powerful ML techniques to a wide range of problems in many relevant fields. Knowledge and experience with these types of techniques are therefore important, not only for researchers in machine learning and computer science, but also for researchers across disciplines as made evident by the recently established AI for the people centre at AAU.
Prerequisites: Basic knowledge in probability theory and machine learning.
Learning objectives: Obtain knowledge and application skills about the use of probabilistic machine learning methods to address real world problems involving complex decision making processes under uncertainty.
Teaching methods: The course will be structured in two consecutive days. Each day will be split into two sessions. Each session will involve and mix between theory and hand-ons exercises.
Criteria for assessment: Students will be asked to build a probabilistic model on a dataset relevant for their PhD project. The probabilistic model should use the elements and methodologies covered in the course.
Key literature: Murphy, Kevin P. Probabilistic machine learning: an introduction. MIT press, 2022.
Organizer: Andres Masegosa
Lecturers: Andres Masegosa
ECTS: 2.0
Time: 12 - 13 March 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 30
Deadline: 19 February
Important information concerning PhD courses:
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: Machine learning (ML) and artificial intelligence have had major impacts on all areas of society and across research disciplines. Probabilistic ML provides a principled approach, based on probabilistic methods, to develop intelligent systems that make optimal decisions under uncertainty. Many problems in science can be casted as decision problems under uncertainty. In consequence, Probabilistic ML allows the application of powerful ML techniques to a wide range of problems in many relevant fields. Knowledge and experience with these types of techniques are therefore important, not only for researchers in machine learning and computer science, but also for researchers across disciplines as made evident by the recently established AI for the people centre at AAU.
Prerequisites: Basic knowledge in probability theory and machine learning.
Learning objectives: Obtain knowledge and application skills about the use of probabilistic machine learning methods to address real world problems involving complex decision making processes under uncertainty.
Teaching methods: The course will be structured in two consecutive days. Each day will be split into two sessions. Each session will involve and mix between theory and hand-ons exercises.
Criteria for assessment: Students will be asked to build a probabilistic model on a dataset relevant for their PhD project. The probabilistic model should use the elements and methodologies covered in the course.
Key literature: Murphy, Kevin P. Probabilistic machine learning: an introduction. MIT press, 2022.
Organizer: Andres Masegosa
Lecturers: Andres Masegosa
ECTS: 2.0
Time: 12 - 13 March 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 30
Deadline: 19 February
Important information concerning PhD courses:
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Welcome to Bayesian Statistics Simulation and Software (2025)
Description: During the last decades, Bayesian statistics has gained enormous popularity as an elegant and powerful computational tool to perform statistical analysis in complex stochastic models as applied in engineering, science and medicine. Bayesian statistics offers an alternative approach to traditional data analysis by including prior knowledge about the model parameters in form of a prior distribution. Using Bayes formula the prior distribution is updated from the posterior distribution by incorporating the observed data by means of the likelihood. Subsequently statistical inference about the unknown model parameters is derived from the posterior distribution. However, the posterior distribution is often intractable due to high-dimensional complex integrals implying that approximate stochastic simulation techniques such as Markov Chain Monte Carlo (MCMC) methods become crucial. This course reviews the basics ideas behind Bayesian statistics and Markov chain Monte Carlo (MCMC) methods. Background on Markov chains will be provided and subjects such as Metropolis and Metropolis-Hastings algorithms, Gibbs sampling and output analysis will be discussed. Furthermore, graphical models will be introduced as a convenient tool to model complex dependency structures within a stochastic model.
Prerequisites: In principle the course is accessible to those new to these subjects, however, some mathematical training will be an advantage and a basic knowledge of statistics and probability theory as obtained through engineering studies at Aalborg University is definitely expected.
Learning objectives: The course aims at providing fundamental knowledge about complex Bayesian models which require MCMC methods. The participants are expected to gain experience with Bayesian methods (prior and posterior distributions, confidence regions, prediction, sensitivity analysis, model criticism), MCMC algorithms (Gibbs sampling, Metropolis-Hastings algorithm, Gibbs within Metropolis-Hastings algorithm) and practical data examples. The course will not be a "a black box approach" to the subject as there will be some mathematical abstraction which is needed in order to construct meaningful Bayesian models and simulation procedures.
Teaching methods: A mixture of lectures and exercises. The theory will be demonstrated through different examples of applications and exercises, partly based on the software package R.
Criteria for assessment: Assessment of the course through active participation and a larger hand-in exercise after the completion of the course.
Key literature: The course does not follow any particular text book, but rather the course slides and a few notes (referred to in the plan) will be the main course material. However, a few pointers to relevant literature can be found in the following. We do not recommend or endorse any of these books in particular, and it is impossible to recommend a book for everyone taking the course given the very diverse background of the participants.
Two introductory level books:
- Peter D. Hoff (2009). A First Course in Bayesian Statistical Methods. Springer.
- Peter M. Lee (2004). Bayesian Statistics: an introduction, 3rd ed. Arnold.
Additional useful textbooks:
- Andrew Gelman, et al. (2014). Bayesian Data Analysis, 3rd ed. CRC Press.
- Jean-Michel Marin and Christian Robert (2007) Bayesian Core: A Practical Approach to Computational Bayesian Statistics. Springer.
- Jean-Michel Marin and Christian Robert (2014) Bayesian Essentials with R. Springer.
- Olle Häggström (2002). Finite Markov Chains and Algorithmic Applications. Cambridge University Press.
- Christian P. Robert and George Casella (2004). Monte Carlo Statistical Methods, 2nd ed. Springer.
- Maria Rizzo (2007). Statistical Computing with R. Chapman & Hall/CRC.
- Ioannis Ntzoufras (2009). Bayesian Modeling Using WinBUGS. Wiley.
Organizer: Jesper Møller
Lecturers: Jesper Møller
ECTS: 5
Time: 12 - 14, 17 - 19 November 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 40
Deadline: 22 October 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: During the last decades, Bayesian statistics has gained enormous popularity as an elegant and powerful computational tool to perform statistical analysis in complex stochastic models as applied in engineering, science and medicine. Bayesian statistics offers an alternative approach to traditional data analysis by including prior knowledge about the model parameters in form of a prior distribution. Using Bayes formula the prior distribution is updated from the posterior distribution by incorporating the observed data by means of the likelihood. Subsequently statistical inference about the unknown model parameters is derived from the posterior distribution. However, the posterior distribution is often intractable due to high-dimensional complex integrals implying that approximate stochastic simulation techniques such as Markov Chain Monte Carlo (MCMC) methods become crucial. This course reviews the basics ideas behind Bayesian statistics and Markov chain Monte Carlo (MCMC) methods. Background on Markov chains will be provided and subjects such as Metropolis and Metropolis-Hastings algorithms, Gibbs sampling and output analysis will be discussed. Furthermore, graphical models will be introduced as a convenient tool to model complex dependency structures within a stochastic model.
Prerequisites: In principle the course is accessible to those new to these subjects, however, some mathematical training will be an advantage and a basic knowledge of statistics and probability theory as obtained through engineering studies at Aalborg University is definitely expected.
Learning objectives: The course aims at providing fundamental knowledge about complex Bayesian models which require MCMC methods. The participants are expected to gain experience with Bayesian methods (prior and posterior distributions, confidence regions, prediction, sensitivity analysis, model criticism), MCMC algorithms (Gibbs sampling, Metropolis-Hastings algorithm, Gibbs within Metropolis-Hastings algorithm) and practical data examples. The course will not be a "a black box approach" to the subject as there will be some mathematical abstraction which is needed in order to construct meaningful Bayesian models and simulation procedures.
Teaching methods: A mixture of lectures and exercises. The theory will be demonstrated through different examples of applications and exercises, partly based on the software package R.
Criteria for assessment: Assessment of the course through active participation and a larger hand-in exercise after the completion of the course.
Key literature: The course does not follow any particular text book, but rather the course slides and a few notes (referred to in the plan) will be the main course material. However, a few pointers to relevant literature can be found in the following. We do not recommend or endorse any of these books in particular, and it is impossible to recommend a book for everyone taking the course given the very diverse background of the participants.
Two introductory level books:
- Peter D. Hoff (2009). A First Course in Bayesian Statistical Methods. Springer.
- Peter M. Lee (2004). Bayesian Statistics: an introduction, 3rd ed. Arnold.
Additional useful textbooks:
- Andrew Gelman, et al. (2014). Bayesian Data Analysis, 3rd ed. CRC Press.
- Jean-Michel Marin and Christian Robert (2007) Bayesian Core: A Practical Approach to Computational Bayesian Statistics. Springer.
- Jean-Michel Marin and Christian Robert (2014) Bayesian Essentials with R. Springer.
- Olle Häggström (2002). Finite Markov Chains and Algorithmic Applications. Cambridge University Press.
- Christian P. Robert and George Casella (2004). Monte Carlo Statistical Methods, 2nd ed. Springer.
- Maria Rizzo (2007). Statistical Computing with R. Chapman & Hall/CRC.
- Ioannis Ntzoufras (2009). Bayesian Modeling Using WinBUGS. Wiley.
Organizer: Jesper Møller
Lecturers: Jesper Møller
ECTS: 5
Time: 12 - 14, 17 - 19 November 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 40
Deadline: 22 October 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Jesper Møller
Welcome to Open Science - Principles Guidelines Infrastructure (2025)
Description: This course introduces PhD students to The principles of Open Sciences and shows them how to implement them in their own PhD projects. Knowledge about open science is crucial for early career researchers for several reasons:
- Transparency and Reproducibility: Open science promotes transparency in research methodologies, data, and findings. This ensures that research can be independently verified and reproduced, which is essential for the credibility and reliability of scientific work.
- Collaboration and Networking: Open science fosters collaboration by making research outputs more accessible. Sharing data, methods, and findings openly allows researchers from different disciplines and institutions to collaborate more easily, leading to innovative and interdisciplinary research.
- Increased Visibility and Impact: Open access publishing and data sharing increase the visibility and impact of research. When research is freely available, it is more likely to be read, cited, and used by other researchers, policymakers, and practitioners, amplifying its reach and significance.
- Ethical Responsibility: Adopting open science practices aligns with ethical standards in research by ensuring that publicly funded research is accessible to the public. It also helps prevent issues related to data fabrication, falsification, and plagiarism.
- Funding and Career Advancement: Many funding agencies and institutions are increasingly requiring or encouraging open science practices. Researchers who are knowledgeable about open science are better positioned to secure funding and comply with these requirements. Additionally, demonstrating a commitment to open science can enhance a researcher's reputation and career prospects.
- Public Engagement and Trust: Open science enhances public trust in scientific research by making it more transparent and accessible. Engaging with the public and stakeholders through open practices helps to demystify research processes and outcomes, fostering greater public understanding and support for science.
- Innovation and Efficiency: Open science accelerates scientific discovery and innovation by reducing duplication of efforts and enabling researchers to build on existing work more efficiently. Sharing data and tools openly can lead to new insights and advancements that might not be possible in a closed research environment.
In an nutshell, participants in this course will learn why embracing open science practices is not only beneficial for their own work but also contributes to the broader goals of advancing science, promoting ethical standards, and fostering a more inclusive and collaborative research community.
Prerequisites: Students should have completed the mandatory PhD courses ("Introduction to the PhD Study" and "Applying the Danish Code of Conduct" or comparable courses) and be far enough into their PhD study to have some data to work with (quantitative or qualitative).
Learning objectives: By the end of this course, PhD students will be able to critically evaluate and apply the principles and practices of open science to their own research. They will develop skills in data sharing, open access publishing, reproducibility, and collaboration, fostering transparency and accessibility in their scholarly work. Students will also gain an understanding of the ethical and legal considerations in open science, and be equipped to advocate for and implement open science practices within their academic communities.
Teaching methods: This course will combine lectures, discussions, and hands-on exercises where students practice how to use a data management plan, research data repositories, and reproducible workflows. We plan to hold it in person at AAU CPH.
Criteria for assessment: Students will be assessed based on an Open Science strategy that they will develop for their own PhD project after this course.
Key literature:
Foster, Erin D., and Ariel Deardorff. "Open science framework (OSF)." Journal of the Medical Library Association: JMLA 105.2 (2017): 203.
Banks, George C., et al. "Answers to 18 questions about open science practices." Journal of Business and Psychology 34 (2019): 257-270.
Wilkinson, Mark D., et al. "The FAIR Guiding Principles for scientific data management and stewardship." Scientific data 3.1 (2016): 1-9.
Burgelman, Jean-Claude. "Politics and open science: How the European Open Science Cloud became reality (the untold story)." Data Intelligence 3.1 (2021): 5-19.
Organizer: Carsten Keßler, Jamal Jokar Arsanjani
Lecturers: Carsten Keßler, Jamal Jokar Arsanjani
ECTS: 2.0
Time: 17 - 18 March 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 20
Deadline: 24 February 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: This course introduces PhD students to The principles of Open Sciences and shows them how to implement them in their own PhD projects. Knowledge about open science is crucial for early career researchers for several reasons:
- Transparency and Reproducibility: Open science promotes transparency in research methodologies, data, and findings. This ensures that research can be independently verified and reproduced, which is essential for the credibility and reliability of scientific work.
- Collaboration and Networking: Open science fosters collaboration by making research outputs more accessible. Sharing data, methods, and findings openly allows researchers from different disciplines and institutions to collaborate more easily, leading to innovative and interdisciplinary research.
- Increased Visibility and Impact: Open access publishing and data sharing increase the visibility and impact of research. When research is freely available, it is more likely to be read, cited, and used by other researchers, policymakers, and practitioners, amplifying its reach and significance.
- Ethical Responsibility: Adopting open science practices aligns with ethical standards in research by ensuring that publicly funded research is accessible to the public. It also helps prevent issues related to data fabrication, falsification, and plagiarism.
- Funding and Career Advancement: Many funding agencies and institutions are increasingly requiring or encouraging open science practices. Researchers who are knowledgeable about open science are better positioned to secure funding and comply with these requirements. Additionally, demonstrating a commitment to open science can enhance a researcher's reputation and career prospects.
- Public Engagement and Trust: Open science enhances public trust in scientific research by making it more transparent and accessible. Engaging with the public and stakeholders through open practices helps to demystify research processes and outcomes, fostering greater public understanding and support for science.
- Innovation and Efficiency: Open science accelerates scientific discovery and innovation by reducing duplication of efforts and enabling researchers to build on existing work more efficiently. Sharing data and tools openly can lead to new insights and advancements that might not be possible in a closed research environment.
In an nutshell, participants in this course will learn why embracing open science practices is not only beneficial for their own work but also contributes to the broader goals of advancing science, promoting ethical standards, and fostering a more inclusive and collaborative research community.
Prerequisites: Students should have completed the mandatory PhD courses ("Introduction to the PhD Study" and "Applying the Danish Code of Conduct" or comparable courses) and be far enough into their PhD study to have some data to work with (quantitative or qualitative).
Learning objectives: By the end of this course, PhD students will be able to critically evaluate and apply the principles and practices of open science to their own research. They will develop skills in data sharing, open access publishing, reproducibility, and collaboration, fostering transparency and accessibility in their scholarly work. Students will also gain an understanding of the ethical and legal considerations in open science, and be equipped to advocate for and implement open science practices within their academic communities.
Teaching methods: This course will combine lectures, discussions, and hands-on exercises where students practice how to use a data management plan, research data repositories, and reproducible workflows. We plan to hold it in person at AAU CPH.
Criteria for assessment: Students will be assessed based on an Open Science strategy that they will develop for their own PhD project after this course.
Key literature:
Foster, Erin D., and Ariel Deardorff. "Open science framework (OSF)." Journal of the Medical Library Association: JMLA 105.2 (2017): 203.
Banks, George C., et al. "Answers to 18 questions about open science practices." Journal of Business and Psychology 34 (2019): 257-270.
Wilkinson, Mark D., et al. "The FAIR Guiding Principles for scientific data management and stewardship." Scientific data 3.1 (2016): 1-9.
Burgelman, Jean-Claude. "Politics and open science: How the European Open Science Cloud became reality (the untold story)." Data Intelligence 3.1 (2021): 5-19.
Organizer: Carsten Keßler, Jamal Jokar Arsanjani
Lecturers: Carsten Keßler, Jamal Jokar Arsanjani
ECTS: 2.0
Time: 17 - 18 March 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 20
Deadline: 24 February 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Jamal Jokar Arsanjani
- Teacher: Carsten Keßler
Welcome to Survey and questionnaire - design use and validation.
Description: This course will focus on using surveys in health science and give participants an understanding of the strengths and limitations of surveys as a study design and the research questions that surveys can and cannot answer. The choice of study population, sampling and data collection will be discussed as well as the advantages and disadvantages of using standardised, previously validated, or self-constructed questionnaires. Furthermore, participants will learn how to construct a questionnaire with high validity and reliability, focusing on both the writing of clear and unbiased questions and the choice of suitable options for answering and different types of measurement scales. Finally, participants will gain an understanding of the importance of thorough testing of questionnaires and of quantitative as well as qualitative methods for this purpose. During the course, lectures and discussions will be mixed with group work and individual exercises, allowing participants to develop, test and discuss questionnaire questions and measurement scales relevant to their own study.
Prerequisites: None
Teaching methods: Lectures, discussion, group exercise and presentation of students' projects
Criteria for assessment: Active participation and presentation of own project with a focus on the topics of the course for the class
Organizer: Henrik Bøggild
Lecturers: Henrik Bøggild
ECTS: 2.0
Time: 28 - 29 April, 6-7 May 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 30
Deadline: 7 April 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: This course will focus on using surveys in health science and give participants an understanding of the strengths and limitations of surveys as a study design and the research questions that surveys can and cannot answer. The choice of study population, sampling and data collection will be discussed as well as the advantages and disadvantages of using standardised, previously validated, or self-constructed questionnaires. Furthermore, participants will learn how to construct a questionnaire with high validity and reliability, focusing on both the writing of clear and unbiased questions and the choice of suitable options for answering and different types of measurement scales. Finally, participants will gain an understanding of the importance of thorough testing of questionnaires and of quantitative as well as qualitative methods for this purpose. During the course, lectures and discussions will be mixed with group work and individual exercises, allowing participants to develop, test and discuss questionnaire questions and measurement scales relevant to their own study.
Prerequisites: None
Teaching methods: Lectures, discussion, group exercise and presentation of students' projects
Criteria for assessment: Active participation and presentation of own project with a focus on the topics of the course for the class
Organizer: Henrik Bøggild
Lecturers: Henrik Bøggild
ECTS: 2.0
Time: 28 - 29 April, 6-7 May 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 30
Deadline: 7 April 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Henrik Bøggild
Welcome to Machine Learning (2025)
Description: Machine learning revolves around creating computer programs that enable machines to learn from examples or experiences. It's an interdisciplinary field at the intersection of computer science, engineering, statistics, and pattern recognition. In recent decades, it has witnessed rapid theoretical progress and extensive real-world applications across various domains. These applications encompass machine perception (like speech recognition and computer vision), natural language processing (including large language models), time-series prediction, sciences, recommendation systems, medical diagnosis and prognosis, autonomous vehicles, predictive maintenance, sentiment analysis, and beyond. Machine learning serves as a driving force behind the ongoing wave of artificial intelligence.
This course offers a comprehensive introduction to machine learning, with the goal of elucidating fundamental methods and their theoretical underpinnings, while also addressing practical machine learning problems such as pattern recognition, prediction, clustering, and generative modeling.
Topics will include:
- Supervised learning methods: logistic regression, support vector machines, neural networks, K-nearest neighbors, and decision trees
- Unsupervised learning and clustering methods: K-means, Gaussian mixture models, Expectation Maximization algorithm, and principal component analysis
- Deep learning methods: deep neural networks, long short-term memory recurrent neural networks, convolutional neural networks, generative adversarial networks, and Transformers.
- Probabilistic graphical models - Reinforcement learning
Prerequisites: Probability and statistics theory (at intermediate level), linear algebra (at intermediate level) and programming skills in Python (at basic level).
Learning objectives: After attending the course, students must:
- have knowledge of supervised and unsupervised learning methods, deep learning, probabilistic graphical models, and Bayesian decision theory.
- be able to apply the taught methods to solve practical engineering problems.
- be able to evaluate and compare these methods within specific applications.
- have competences in analyzing given problems and identifying appropriate machine learning methods to them.
- have competences in understanding the strengths and limitations of these methods.
Teaching methods: The course will feature a combination of lectures, demonstrations of applications, and hands-on exercises.
Criteria for assessment: Evaluation will be conducted through participation in lectures and submission of exercise solutions.
Key literature:
Machine Learning ? A Probabilistic Perspective, Kevin P. Murphy, The MIT Press, 2012
Introduction to Machine Learning ? second edition, Ethem Alpaydin, The MIT Press, 2009
Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006
Pattern Classification, Second Edition, Richard O. Duda, Peter E. Hart, David G. Stork, Wiley Interscience, 2001.
Machine Learning: A Bayesian and Optimization Perspective, Sergios Theodoridis, Academic Press, 2020.
Organizer: Zheng-Hua Tan
Lecturers: Zheng-Hua Tan
ECTS: 3.0
Time: 17, 19, 21, 24 and 26 March 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 50
Deadline: 24 February 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: Machine learning revolves around creating computer programs that enable machines to learn from examples or experiences. It's an interdisciplinary field at the intersection of computer science, engineering, statistics, and pattern recognition. In recent decades, it has witnessed rapid theoretical progress and extensive real-world applications across various domains. These applications encompass machine perception (like speech recognition and computer vision), natural language processing (including large language models), time-series prediction, sciences, recommendation systems, medical diagnosis and prognosis, autonomous vehicles, predictive maintenance, sentiment analysis, and beyond. Machine learning serves as a driving force behind the ongoing wave of artificial intelligence.
This course offers a comprehensive introduction to machine learning, with the goal of elucidating fundamental methods and their theoretical underpinnings, while also addressing practical machine learning problems such as pattern recognition, prediction, clustering, and generative modeling.
Topics will include:
- Supervised learning methods: logistic regression, support vector machines, neural networks, K-nearest neighbors, and decision trees
- Unsupervised learning and clustering methods: K-means, Gaussian mixture models, Expectation Maximization algorithm, and principal component analysis
- Deep learning methods: deep neural networks, long short-term memory recurrent neural networks, convolutional neural networks, generative adversarial networks, and Transformers.
- Probabilistic graphical models - Reinforcement learning
Prerequisites: Probability and statistics theory (at intermediate level), linear algebra (at intermediate level) and programming skills in Python (at basic level).
Learning objectives: After attending the course, students must:
- have knowledge of supervised and unsupervised learning methods, deep learning, probabilistic graphical models, and Bayesian decision theory.
- be able to apply the taught methods to solve practical engineering problems.
- be able to evaluate and compare these methods within specific applications.
- have competences in analyzing given problems and identifying appropriate machine learning methods to them.
- have competences in understanding the strengths and limitations of these methods.
Teaching methods: The course will feature a combination of lectures, demonstrations of applications, and hands-on exercises.
Criteria for assessment: Evaluation will be conducted through participation in lectures and submission of exercise solutions.
Key literature:
Machine Learning ? A Probabilistic Perspective, Kevin P. Murphy, The MIT Press, 2012
Introduction to Machine Learning ? second edition, Ethem Alpaydin, The MIT Press, 2009
Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006
Pattern Classification, Second Edition, Richard O. Duda, Peter E. Hart, David G. Stork, Wiley Interscience, 2001.
Machine Learning: A Bayesian and Optimization Perspective, Sergios Theodoridis, Academic Press, 2020.
Organizer: Zheng-Hua Tan
Lecturers: Zheng-Hua Tan
ECTS: 3.0
Time: 17, 19, 21, 24 and 26 March 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 50
Deadline: 24 February 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Zheng-Hua Tan
Welcome to System Dynamics for Modeling Complex Systems 2025
Description: Systems are getting increasingly complex every day and their interdependency is considerably growing. Therefore, effective modeling approaches are required to improve our understanding of their behavior and investigate the long-term consequences of our decisions. System dynamics (SD) is a powerful modeling tool to understand and analyze the dynamic behavior of complex systems over time. Using SD, the interactions and dependencies within a complex system can be captured to simulate the long-term behavior of the system and analyze the impact of different strategies and decisions. SD incorporates key concepts including positive/negative feedback loops, stocks and flows, delays, and non-linearities to predict system behavior over time using cause and effect analysis, thus providing an effective tool for policymaking. SD is used in many applications and real-world scenarios, particularly economics, healthcare, engineering, ecology, as well as business and management among others. SD is an effective tool for enhancing our system thinking and can improve our everyday decision-making by increasing our awareness about the long-term impacts of our choices.
Prerequisites: Familiarity with basic mathematics, statistics, data analysis, and software skills.
Learning objectives: To enhance system thinking and problem-solving skills. - To equip students with the knowledge and skills to understand, model, and analyze the structure and behavior of complex systems. - To understand and identify positive/negative feedback loops in complex systems structures. - To investigate the long-term impact of strategic decisions on the system behavior.
Teaching methods: Lectures - Self-study - Group work
Criteria for assessment: Active participation in the course - Reports and exercises to be prepared in groups and delivered two weeks after closing the course.
Key literature:
Books: Business Dynamics: Systems thinking and modeling for a complex world, John D. Sterman, McGraw-Hill,2000
Papers:
1. Blumberga A, Lauka D, Barisa A, Blumberga D. Modelling the Baltic power system till 2050. Energy Conversion and management. 2016 Jan 1;107:67-75.
2. Mutingi, M., Mbohwa, C. and Kommula, V.P., 2017. System dynamics approaches to energy policy modelling and simulation. Energy Procedia, 141, pp.532-539.
3. Akhwanzada, S.A. and Tahar, R.M., 2012. Strategic forecasting of electricity demand using system dynamics approach. International Journal of Environmental Science and Development, 3(4), p.328.
4. Pereira, A.J. and Saraiva, J.T., 2013. A long term generation expansion planning model using system dynamics?Case study using data from the Portuguese/Spanish generation system. Electric Power Systems Research, 97, pp.41-50
5. Gravelsins A, Bazbauers G, Blumberga A, Blumberga D, Bolwig S, Klitkou A, Lund PD. Modelling energy production flexibility: system dynamics approach. Energy Procedia. 2018 Aug 1;147:503-9.
6. Esmaieli M, Ahmadian M. The effect of research and development incentive on wind power investment, a system dynamics approach. Renewable energy. 2018 Oct 1;126:765-73.
7. Kunsch, P.L., 2008. Simulation of micro-CHP diffusion by means of system dynamics. Energy Policy, 36(7), pp.2308-2319.
Organizer: Najmeh Bazmohammadi
Lecturers: Najmeh Bazmohammadi
ECTS: 2.0
Time: 20 - 21 October 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 20
Deadline: 29 September 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: Systems are getting increasingly complex every day and their interdependency is considerably growing. Therefore, effective modeling approaches are required to improve our understanding of their behavior and investigate the long-term consequences of our decisions. System dynamics (SD) is a powerful modeling tool to understand and analyze the dynamic behavior of complex systems over time. Using SD, the interactions and dependencies within a complex system can be captured to simulate the long-term behavior of the system and analyze the impact of different strategies and decisions. SD incorporates key concepts including positive/negative feedback loops, stocks and flows, delays, and non-linearities to predict system behavior over time using cause and effect analysis, thus providing an effective tool for policymaking. SD is used in many applications and real-world scenarios, particularly economics, healthcare, engineering, ecology, as well as business and management among others. SD is an effective tool for enhancing our system thinking and can improve our everyday decision-making by increasing our awareness about the long-term impacts of our choices.
Prerequisites: Familiarity with basic mathematics, statistics, data analysis, and software skills.
Learning objectives: To enhance system thinking and problem-solving skills. - To equip students with the knowledge and skills to understand, model, and analyze the structure and behavior of complex systems. - To understand and identify positive/negative feedback loops in complex systems structures. - To investigate the long-term impact of strategic decisions on the system behavior.
Teaching methods: Lectures - Self-study - Group work
Criteria for assessment: Active participation in the course - Reports and exercises to be prepared in groups and delivered two weeks after closing the course.
Key literature:
Books: Business Dynamics: Systems thinking and modeling for a complex world, John D. Sterman, McGraw-Hill,2000
Papers:
1. Blumberga A, Lauka D, Barisa A, Blumberga D. Modelling the Baltic power system till 2050. Energy Conversion and management. 2016 Jan 1;107:67-75.
2. Mutingi, M., Mbohwa, C. and Kommula, V.P., 2017. System dynamics approaches to energy policy modelling and simulation. Energy Procedia, 141, pp.532-539.
3. Akhwanzada, S.A. and Tahar, R.M., 2012. Strategic forecasting of electricity demand using system dynamics approach. International Journal of Environmental Science and Development, 3(4), p.328.
4. Pereira, A.J. and Saraiva, J.T., 2013. A long term generation expansion planning model using system dynamics?Case study using data from the Portuguese/Spanish generation system. Electric Power Systems Research, 97, pp.41-50
5. Gravelsins A, Bazbauers G, Blumberga A, Blumberga D, Bolwig S, Klitkou A, Lund PD. Modelling energy production flexibility: system dynamics approach. Energy Procedia. 2018 Aug 1;147:503-9.
6. Esmaieli M, Ahmadian M. The effect of research and development incentive on wind power investment, a system dynamics approach. Renewable energy. 2018 Oct 1;126:765-73.
7. Kunsch, P.L., 2008. Simulation of micro-CHP diffusion by means of system dynamics. Energy Policy, 36(7), pp.2308-2319.
Organizer: Najmeh Bazmohammadi
Lecturers: Najmeh Bazmohammadi
ECTS: 2.0
Time: 20 - 21 October 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 20
Deadline: 29 September 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Najmeh Bazmohammadi
- Teacher: Najmeh Bazmohammadi
Welcome to BIOSTATISTICS II
Description: This course on biostatistics focused on the complexity of data collected in biomedical research. Therefore, the course focused on topics like sample size estimation, meta-analysis and multi-factorial methods.
Prerequisites: BIOSTATISTICS I or equivalent
Learning objectives:
1.The ability to understand the assumptions and perform the following statistical tests: Multifactorial ANOVA, Repeated measures ANOVA, Multiple and non-linear regression, Survival Analysis
2. Understand power and sample-size calculation (sample-size considerations), and perform then in the context of your own studies
3. Understand and perform meta-analyses in systematic reviews.
4. Write and a statistical analysis plan preferably based your data
5. Perform the statistical analysis according to the statistical analysis plan and present the results
Teaching methods:
This course consisted of two parts:
1) a review of some well-known and widely used parametric and non-parametric methods and discussions of basic designs of experimental studies, 2) a practical part where the focus is on applying the methods to relevant and realistic data sets collected from medical and biomedical research.
Criteria for assessment: The evaluation of the course is based on the statistical analysis plan and the presentation of the results.
Key literature: Selected papers and statistical text book chapters
Organizer: Carsten Dahl Mørch
Lecturers: Carsten Dahl Mørch
ECTS: 3.5
Time: 21, 28 October, 4, 11, 18, 25 November and 2 December 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Number of seats: 24
Deadline: 30 September 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: This course on biostatistics focused on the complexity of data collected in biomedical research. Therefore, the course focused on topics like sample size estimation, meta-analysis and multi-factorial methods.
Prerequisites: BIOSTATISTICS I or equivalent
Learning objectives:
1.The ability to understand the assumptions and perform the following statistical tests: Multifactorial ANOVA, Repeated measures ANOVA, Multiple and non-linear regression, Survival Analysis
2. Understand power and sample-size calculation (sample-size considerations), and perform then in the context of your own studies
3. Understand and perform meta-analyses in systematic reviews.
4. Write and a statistical analysis plan preferably based your data
5. Perform the statistical analysis according to the statistical analysis plan and present the results
Teaching methods:
This course consisted of two parts:
1) a review of some well-known and widely used parametric and non-parametric methods and discussions of basic designs of experimental studies, 2) a practical part where the focus is on applying the methods to relevant and realistic data sets collected from medical and biomedical research.
Criteria for assessment: The evaluation of the course is based on the statistical analysis plan and the presentation of the results.
Key literature: Selected papers and statistical text book chapters
Organizer: Carsten Dahl Mørch
Lecturers: Carsten Dahl Mørch
ECTS: 3.5
Time: 21, 28 October, 4, 11, 18, 25 November and 2 December 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Number of seats: 24
Deadline: 30 September 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Carsten Dahl Mørch
Welcome to Biostatistics I
Description: This is a basic course in statistics, and it focuses on the application of statistics to describe and analyze data (i.e., descriptive, and inferential statistics). The course will use different examples from e.g., medicine, biology, and biomedical engineering and focus on the application of statistics to describe data and test hypotheses, and less so on the underlying mathematical calculations.
Prerequisites: None. This is a fundamental course.
Learning objectives:
1. Write a draft of an SAP for your own research project.
2. The ability to understand the assumptions and perform the following statistical tests: - Descriptive statistic - Probability distributions - Estimation - Hypothesis testing - parametric tests - Non-parametric tests - One-way ANOVA - Cross-tabulations - Regression and correlation
Teaching methods:
This course will consist of four parts:
1) relevant description and presentation of data pending your data type
2) a review of some well-known and widely used parametric and non-parametric methods and discussions of basic designs of experimental studies
3) application of the statistical methods for hypothesis testing
4) developing a statistical analysis plan (SAP) for your research project.
Criteria for assessment: The written statistical analysis plan and its presentation will form the basis of the evaluation of the course.
Key literature: B. Kirkwood, J. Sterne, ?Essential medical statistics?. Plus handouts.
Organizer: Carsten Dahl Mørch
Lecturers: Carsten Dahl Mørch
ECTS: 3.5
Time: 4 ,11 18, 25 March and 1, 8, 22 April 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Number of seats: 24
Deadline: 11 February 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: This is a basic course in statistics, and it focuses on the application of statistics to describe and analyze data (i.e., descriptive, and inferential statistics). The course will use different examples from e.g., medicine, biology, and biomedical engineering and focus on the application of statistics to describe data and test hypotheses, and less so on the underlying mathematical calculations.
Prerequisites: None. This is a fundamental course.
Learning objectives:
1. Write a draft of an SAP for your own research project.
2. The ability to understand the assumptions and perform the following statistical tests: - Descriptive statistic - Probability distributions - Estimation - Hypothesis testing - parametric tests - Non-parametric tests - One-way ANOVA - Cross-tabulations - Regression and correlation
Teaching methods:
This course will consist of four parts:
1) relevant description and presentation of data pending your data type
2) a review of some well-known and widely used parametric and non-parametric methods and discussions of basic designs of experimental studies
3) application of the statistical methods for hypothesis testing
4) developing a statistical analysis plan (SAP) for your research project.
Criteria for assessment: The written statistical analysis plan and its presentation will form the basis of the evaluation of the course.
Key literature: B. Kirkwood, J. Sterne, ?Essential medical statistics?. Plus handouts.
Organizer: Carsten Dahl Mørch
Lecturers: Carsten Dahl Mørch
ECTS: 3.5
Time: 4 ,11 18, 25 March and 1, 8, 22 April 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Number of seats: 24
Deadline: 11 February 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Carsten Dahl Mørch