VT2022, 1.5 ECTS or 6 ECTS
Tentative courses (changes may still occur)
Reproducible and Interactive Data Analysis and Modelling using Jupyter Notebooks (4 ECTS, 2022, term still undecided)
Image analysis (7.5 ECTS, term still undecided)
The aim of the course is to give necessary knowledge of digital image analysis for further research within the area and to be able to use digital image analysis within other research areas such as computer graphics, image coding, video coding and industrial image processing problems. The aim is also to prepare the student for further studies in e.g. computer vision, multispectral image analysis and statistical image analysis.
Parallel programming of HPC systems (7.5 ECTS, HT2022)
The course discusses programming techniques required to efficiently utilise parallel computing in a computational research project in science or engineering. The course will discuss shared memory and distributed memory parallelisation in a C, C++ and Fortran context. Widely utilised parts of the application interfaces of OpenMP and MPI will be introduced during the course. The course will discuss commonly encountered issues in parallel programming, such as data-races and dead-lock and show techniques required to avoid these issues.
Common programming tools will be introduced and demonstrated. This includes parallel debuggers to analyse issues concerning code correctness as well parallel profilers which are extremely helpful, when it comes to understanding performance problems in parallel and serial applications.
Participants should be able to write simple programs in one or more of C, C++ or Fortran.
AI for Medicine and Life Sciences - Introduction (1.5 ECTS, HT2022)
Artificial intelligence is rapidly entering medicine and life science research as well as pharmaceutical industry and health care institutions. This introduction course will give an overview over artificial intelligence concepts and methods and over current and future applications of artificial intelligence in medicine and life sciences. Societal, ethical and legal challenges will also be addressed. In addition, students will hear about ongoing research in this area at Lund University and develop a project plan for an artificial intelligence research project in their own research domain. This is the first course in the new course package on "Artificial Intelligence in Medicine and Life Science" and will not have any programming exercises. It will be followed by several in-depth courses with practical exercises, each focusing on different types of data.
Other courses of interest to COMPUTE PhD students
Graduate course "Bayesian Analysis and Decision Theory" (NAMV005, 5 credits)
Open for registration.
There will be five meetings in January-February (four of them online and the last one on campus in Lund). PhD students, who are part of the ClimBECO research school will be given priority, but students from other research schools are weelcome as well. The course introduces Bayesian analysis using Stan (for MCMC sampling) and R (as an interface to Stan and a data analysis platform). The major part of the course covers Bayesian data analysis and statistical inference. Bayesian analysis is also put into the context related to subjective probability to quantify uncertainty and Bayesian decision theory. Application is open until 24 December or when the slots are filled (whichever comes first). More information about the course and a link to the registration form can be found at the "Bayesian Analysis and Decision Theory" course page in Canvas.
Iterative Solution of Large Scale Systems in Scientific Computing
Planned for VT 2022
For more details, see the course page of "Iterative Solution of Large Scale Systems in Scientific Computing" at the Centre for Mathematical Sciences or contact Philipp Birken email@example.com.