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Compute

Lund University

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Courses

This page lists upcoming COMPUTE courses and other courses of interest to COMPUTE students.

Previous courses 2012-2020 and previous courses 2021- are found on separate pages.

It is typically not possible to admit MSc students to COMPUTE courses.

Upcoming courses

Tentative courses (changes may still occur)

Reproducible and Interactive Data Analysis and Modelling using Jupyter Notebooks (4 ECTS, 2022, term tbc)

The aim of this course is to introduce students to the Jupyter Notebook which is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more. Through the notebooks, research results and the underlying analysis can be transparently reproduced as well as shared.

Description of last course instance

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.

Prerequisites

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.

Description of last course instance

Scientific Computing with Python and Fortran (7.5 ECTS, HT2022)

This course is intended for students with basic knowledge of programming in any language who would like to learn the techniques of scientific programming. The course covers scientific programming in Python, including writing numerical codes with NumPy, data handling, visualisation with Matplotlib and ParaViews, writing user interfaces with Qt, and creating Python environments for scientific applications. It also covers using the compiled language Fortran, stand-alone or via mixed-language programming with Python.

For students without basic programming knowledge in C, C++ or Fortran this course will equip you with the required prerequisites for the course "Parallel programming of HPC systems

Description of last course instance

AI in Medicine and the Life Sciences – AI for text and language data (1.5/6.0 ECTS, term tbc)

Artificial intelligence (AI), and deep learning in particular, is rapidly entering medicine and life science research as well as pharmaceutical industry and health care institutions. One of the areas it is applied to is the analysis or generation of text and language data, for example to extract information from patient records or diagnostic questionnaires, summarize research literature, automate journalling, operate medical chat bots or gain public health insights from social media.

Other courses of interest to COMPUTE PhD students

To be announced

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