Description:
Modern laboratory equipment produces huge amounts of experimental data — spectral vectors with hundreds of wavelengths, microarrays, gene expression data, sensors, multi-channel images and many others. Even conventional measurements may end up with tens to hundreds of variables. Such data represent a wealth of potential information but usually only a part of it relates to a problem of interest.
This course teaches how to extract problem-dependent information from multivariate data. The practical part of the course assuming using R for calculations and visualization of results.
The course is split in to two parts. The first part (3 days, 2 ECTS) introduces descriptive and inferential statistics, as well as data exploration with Principal Component Analysis. The second part (3 days, 2 ECTS) is mainly devoted to supervised analysis of multivariate data, including regression and validation, preprocessing and variable selection as well as classification.
In each part lectures are supplemented with a suite of real life examples and exercises as well as assignments, with which students will try the discussed methods by solving various data analysis problems. To complete the course, participants have to work on three mini-projects and submit their results in form of reports within 1 month after the main part is finished.Organizer: Associate Professor Sergey Kucheryavskiy, E-mail svk@bio.aau.dk
Lecturers: Associate Professor Sergey Kucheryavskiy, E-mail svk@bio.aau.dk
ECTS: 4 (2+2)
Time: November 15-17 (1st part), 20-22 (2nd part), 2023
Place: Section for Chemistry and Chemical Engineering, Department of Chemistry and Bioscience, Aalborg University, campus Esbjerg,
Niels Bohrs vej, 8, 6700, Esbjerg, Denmark
City: Esbjerg
Number of seats:
Deadline: 10 November 2023
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.
- Teacher: Sergey Kucheryavskiy
Description:
Disordered materials, such as inorganic glasses, polymer glasses, cement hydrates, amorphous membrane, metal-organic framework glasses, and gels, are critical to our sustainable future. Their design has traditionally been done using the time-consuming and inefficient “trial-and-error” approach. However, new approaches relying on artificial intelligence, machine learning, and topological data analysis offer opportunities for accelerating the discovery of advanced disordered materials. For the planned Ph.D. course, we will introduce data science methods relevant to disordered materials, including various types of machine learning algorithms, data mining and natural language processing methods, persistent homology and so.
Several distinguished lecturers will be invited to present the fundamental principles of these methods as well as their recent progress in translating this knowledge into applications with relevance for new material development.
The students will communicate with both fellow students and lecturers, read the pre-assigned literature, and do assignments from lecturers through group work.
Organizer: Professor Morten M. Smedskjær, mos@bio.aau.dkProfessor Yuanzheng Yue, yy@bio.aau.dk
Lecturers: External speakers
ECTS: 2
Time: August 16 - 18 2023
Place: Department of Chemistry and Bioscience, Aalborg University (AAU), Fredrik Bajers Vej 7H, DK-9220 Aalborg, Denmark
City: Aalborg
Number of seats: Ca. 17 (AAU) and 5-10 (Non-AAU)
Deadline: June 1
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.
- Teacher: Morten Mattrup Smedskjær
- Teacher: Yuanzheng Yue
Advanced structure elucidation by spectroscopy
General objective
This is a one week tutorial (guided problem solving). After a brief introduction of how to extract information from mass spectrometry (high resolution) and NMR spectroscopy (1D and 2D correlation spectra) the interpretation will be trained on exercises of variable complexity. The exercises are real spectra and will include some of the pitfalls, challenges and artifacts typical for real samples.
Learning objective
The participant will learn how to interpret NMR spectra from structurally well-defined samples (assignment) and how to deduce the unknown chemical topology from NMR spectra by solving problems with a mild guidance of the tutor. The practical work will typically be done in small groups (2 up to 4).
The learning speed will be individually adjusted by choosing the adequate complexity of the next task (exercise).
Exercises will be available as printouts and in electronic data (Bruker format) for using own software.
This course, however, will not offer guidance how to use software for processing NMR spectra or software for using NMR spectra for characterization or elucidation of structures. The participants should finally understand how such computer aided structure elucidation (by the combination of NMR correlation(s) and the information of chemical shifts) works and be able to judge its limitations.
One of the structure elucidation(s) will be documented by the participants (written form) with a full description of how the structure hypothesis has been derived.
Contents
The course will introduce a set of 2D NMR experiments (homo- and hetero-nuclear) required for the spectroscopic characterization (of synthetic samples and isolates of unknown chemical structure, i.e.: topology)).
The focus is on the concept of information as obtained by
- high resolution mass spectra and the isotope cluster and
- by NMR-correlation experiments.
A formal description of structure elucidation (determining the chemical topology from observed spin-topologies) in terms of information theory will enable the participants to understand how structural elucidation (computer aided and done by humans) works and judge the quality and reliability of a structure determination. The format of a tutorial, i.e. solving problems with some guidance will train the own way of working on practical problems of structure verification and elucidation.
Participants
The course addresses PhD students and students working on their master thesis in
- synthetic organic chemistry
- natural product research
- forensic and pharmaceutical sciences
- other disciplines with interest in the confirmation and elucidation of the structure of organic compounds by NMR spectrospopy (with some help from mass spectrometry)
Requirements will be
- basic knowledge of NMR spectroscopy
- basic knowledge of chemical structures (organic chemistry)
Organizer: Professor Reinhard Wimmer, rw@bio.aau.dk
Lecturers: Adjunct Associate Professor Herbert Kogler, AAU
ECTS: 4
Time: 28 August - 1 September 2023
Place: Aalborg University
Deadline: 30 April 2023
- Teacher: Reinhard Wimmer