Welcome to Mixed Models with Biomedical and Engineering Applications (2024)

Description: Mixed models provide a flexible framework for analyzing data with multiple sources of random variation and they are indispensable in many medical, biological, and engineering applications. When treatments are tested in medical applications, the responses for individuals receiving the same treatment often vary due to unobserved genetic factors and this variation must be taken into account when comparing ifferent treatments. Similarly, in agricultural field trials, random soil variation affects the yield within plots. In quality control applications, the variability of the output of a production process may, apart from random noise, e.g. depend on the batches of raw material used and the employee involved in the manufacturing process. 

The course will provide an introduction to statistical analysis with linear mixed models. Linear mixed models is a unified framework for classical random effects ANOVA models, random coefficient models and linear models for longitudinal data with associated user-friendly implementations in R and SPSS. Linear mixed models moreover provide generalizations of the classical models to complex data not covered by he standard statistical toolbox. 

The course will focus on modeling with mixed models, on how a statistical analysis can be carried out for a mixed model, and on interpretation of models and results. Hands-on experience with real data will be obtained through computer exercises.

Prerequisites: A basic knowledge of statistics (linear regression) and probability theory (random variables, expectation variance and covariance) is expected.

Organizer: Professor Rasmus Waagepetersen - rw@math.aau.dk

Lecturers: Professor Rasmus Waagepetersen - rw@math.aau.dk

ECTS: 1.5

Time: 23 and 27 September 2024

Place: Aalborg University

Number of seats: 20

Deadline: 02 September 2024

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce 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 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. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

For external PhD students: This course is a general course and is prioritized 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.

Welcome to Signal and Spectral Analysis: extracting information from noisy data (2024)


Description: 
In many situations, a number of observations are made which contain some information about an underlying phenomenon we are interested in. Examples of this are:


• The diagnosis of the Parkinson’s disease from a telephone recording,
• The assessment of bearing wear from vibrational data,
• The automatic transcription of music,
• Order tracking analysis of rotating machines,
• Automated analysis of the heart sound, and
• Harmonic analysis in power systems.

To solve these and many other problems, a signal analysis toolbox is needed. This course focuses ondeveloping, explaining, understanding, and using such tools. Specifically, the course covers important and general concepts such as:

• Signal modelling: Which models exist and what are their applicability and limitations?
• Spectral analysis: Why is signal analysis often performed as a function of frequency and how do you do it?
• Inference and parameter estimation: How do you estimate model parameters accurately and quantify how well you do?

The course is primarily developed for doctoral students from medicine and various engineering and natural science disciplines who wish to not only apply, but also to understand signal and spectral analysis. Consequently, the course is rooted in a principled and systematic exposition of fundamental concepts and tools and in a scientific approach which promotes the creation of knowledge over improving state-of-the-art by ε percent. An important goal of the course is to make doctoral students able to solve a signal and spectral analysis task based on data from their own Ph.D.-project. This is integrated in the course via a mini project.

Keywords: Filtering, statistical signal processing, estimation theory, maximum likelihood, powerspectral density estimation, modelling, least squares, autoregressive, nonnegative matrix factorizations, sparsity, periodic signals, Fourier analysis, line spectra.

Prerequisites: Basic probability theory, linear algebra, signal processing, and experience with MATLAB and/or Python programming.


Organizer: Assoc. Professor Jesper Rindom Jensen - jrj@es.aau.dk

Lecturers:
Assoc. Professor Jesper Rindom Jensen - jrj@es.aau.dk

ECTS: 3.0

Time: 24 September, 01, 08, 15 and 23 October 2024

Place: Aalborg University 

Zip code: 9220

City: Aalborg

Teams Channel: LINK

Number of seats: 30

Deadline: 03 September 2024

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce 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 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. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your reregistration.

For external PhD students: This course is a general course and is prioritized 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.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

The course is fully booked! Please sign up at the waiting list. 

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk.


Welcome to Project Management and Interpersonal Skills - A (2024)


Description: This course is a practical ‘hands on’ workshop, which will support PhD students in their endeavors to manage the PhD project. The focus is twofold: 1) enhancing project management skills in a university culture that is characterized by unruly creative work, lack of formal structures and network organization. 2) Enhancing interpersonal skills, particularly in relation to the cooperation with supervisors and other important stakeholders.


Research, long term experience as PhD coach and feed back from earlier participants at this course show that PhD students benefit immensely from being challenged to reflect on, discuss and experiment with their planning practice and the way they approach cooperation with supervisors and other important stakeholders.

The workshop will introduce and illustrate a method on how to plan your time on a daily and long term basis inspired by Steven Covey (2005) and the game plan approach from the world of coaching. The course will also work with the negotiation and conflict management approach from the Harvard Negotiation project (Stone, Patton and Heen, 2010) as a tool to explore interpersonal communication structure. Key issues in the relationship between PhD student and supervisor will be identified and analyzed by means of practical exercises.

There will be a possibility for an individual follow up coaching session after a month for those PhD students that find it beneficial.  

 Target group:

• PhD students preferably during their first year

Time schedule for the workshop: One workshop will be held in the spring and one in the fall

 After the course the PhD student should be able to:

• Understand the challenges of project planning as a PhD student
• Reflect on ones own planning practice and the specific pittfalls and resources one poses
• Develop strategies to address those pittfalls and provide feedback to other PhD students
• Practice a more realistic and efficient planning
• Understand the individual needs and challenges in specific cooperations with supervisors and other important stakeholders
• Apply important negotiating skills 

 Course structure:

• 2 consecutive days consisting of a mix of introductions and exercises individually and in groups.
• Two assignments related to actual skills is to be send in before the workshop

Completion of the course: Handing in assignments and participating in the course are criteria for passing the course.

Course program: A detailed program will be send by email upon acceptance and in due time for preparations.

Course literature (Primary in bold): 
Covey, S.R. (2005). The seven habits of highly effective people. ISBN 9780743268165

Danish Centre for conflict resolution (1996).  Conflict-Solving: Simple and Difficult. Uploaded below

Conflict resolution. A brief introduction (No date, no author) Uploaded below

Patton, B. (2017). You can’t win by avoiding difficult conversations in Journal of Business & Industrial Marketing 3274, 553-557. Emerald Publishing Limited. ISSN 0885-8624

Heim, C. (2012). Tutorial facilitation in the humanities based on the tenets of Carl Rogers in Higher Education 63: 289-298. Springer. DOI 10.1007/s10734-011-9441-z

Prochaska, J. O. , Norcross, J.C and Diclemente, C.C. (2007). Changing for good.  A Revolutionary Six-Stage Program for Overcoming Bad Habits and Moving Your Life Positively Forward. HarperCollins Publishers, New York, USA.

Stone, D., Patton, B. and Heen, S. (2010). Difficult conversations. How to discuss what matters most. Penguin Books. London, England.

Organizer: Pia Bøgelund - pb@plan.aau.dk

Lecturers: Pia Bøgelund - pb@plan.aau.dk and Henrik Worm Routhe - routhe@plan.aau.dk

ECTS: 2.0

Time: 07 and 08 May 2024 

Place: Aalborg University Rendsburggade 6 room 271

Zip code: 9000

City: Aalborg

Number of seats: 20

Deadline: 16 April 2024

NB This course is only for PhD Students at Aalborg University

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce 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 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. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.




For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

Welcome to Data Science using R (2024)


Description:

Most quantitative research projects involve case-specific programming tasks as well as data analysis of a more standard nature. When working with quantitative data it is essential to be able to do so in a systematic and reproducible manner with trustworthy software. This course, Data Science using R, aims at introducing the free program R as a computational environment for integrating these tasks. R has for two decades been a leading tool for computing with data and it is the preferred language for implementation of new statistical methodology.

The course will cover the practice of scientific computing, programming, and quantitative analyses as well as the essential theoretical underpinnings. Topics of the course will include:

• Introducing R as a statistical programming environment for data analysis.
• Efficient data management and high-level graphics using R
• Statistical modelling in theory and practice using R.
• Learning from large datasets using R.
• Aspects of scientific computing in theory and practice.
• Reproducible research in practice.
• Programming in R.

Prerequisites (important):

Participants must have a working knowledge of elementary statistical methods such as regression models and analysis of variance at the level taught e.g. in the "applied statistics" course described at http://asta.math.aau.dk/.

Learning objectives:

After completing the course the participants will:

1. be able to solve programming tasks with R.

2. know how to manage data, visualize data and fit models to data using R.

3. have learned enough about R to continue learning on their own.

Teaching methods:

A combination of instructive videos, computer practicals and lectures.

Course organization:

The course gives 4 ECTS credits which means that the workload of the course is about 120 hours. The course is organized in three blocks each consisting of two days. The blocks are two weeks apart. Prior to the first block and between the blocks there will be tasks for participants to work on, either independently or in smaller groups. After the last block, there will be time to work on the major exercise to be handed in.

Criteria for assessment:

Active participation in the practicals + approval of major exercise (to be handed in after the course).

Frequently asked questions:

• Q: I am from outside AAU and wish to sign up for the course. What do I do?
• A: You click on \"signup\" and fill out the form.
• Q: I can not get the steps above to work. Who can help?
• A: You will have to ask the doctoral school: aauphd@adm.aau.dk
• Q: If I participate in the course, can you then help me analyze a dataset that I work with as part of my ph.d. project.
• A: No, I am afraid that this is not possible
• Q: I am not a ph.d. student, but I would like to participate in the course anyway. Is that possible?
• A: You will have to ask the doctoral school: aauphd@adm.aau.dk
• Q: I realize that I am late for enrollment, but I would really like to participate. Is it possible.
• A: You will have to ask the doctoral school: aauphd@adm.aau.dk  

Organizers: Søren Højsgaard and Ege Rubak 

ECTS: 

4

Time: 

13, 14 March, 3, 4, 24, 25 April 2024

Place: 

Aalborg University - Fredrik Bajers Vej 7C room 3-204

Zip code: 

9220

City: 

Aalborg

Number of seats: 

40

Deadline: 

21 February 2024

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk


Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce 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 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 three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

For external PhD students: This course is a general course and is prioritized 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.