This offering is not in the current catalogue. The information is retained from an earlier publication. Check the provider's current offering.
University of Gothenburg
Advanced simulation and machine learning
The course covers a selection of machine learning algorithms and statistical methods for simulating physical systems. The course is based on a set of projects, which are accompanied by lectures, and hands-on computer exercises. During the course, the students will be exposed to advanced scientific research problems,…
- Higher education
- Information unavailable
- 4 November 2026
- Information unavailable
- Information unavailable
- 50 %
Overview
The course covers a selection of machine learning algorithms and statistical methods for simulating physical systems. The course is based on a set of projects, which are accompanied by lectures, and hands-on computer exercises. During the course, the students will be exposed to advanced scientific research problems, with the aim to reproduce state-of-the-art scientific results. The students will use e.g. the Python programming language and relevant open-source libraries, and will learn to develop and structure computer codes for carrying out scientific and statistical data analyses.
Admission scores
Entry requirements
Bachelors degree in physics or equivalent. Recommended courses: Learning from data and Computational physicsor equivalent. Applicants must prove their knowledge of English: English 6/English B from Swedish Upper Secondary School or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.
The text is reproduced from the Susa source. Antagningsdata does not map GY11 and GY25 or assess personal eligibility.
Source, measure and data quality
- Source
- Skolverket Susa-navet
- Period
- 2026-11-04
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.gu.fym345.11018.20262
- Last checked
- 2026-09-23T10:36:47.551594+00:00
- Limitation
- Antagningsdata does not map GY11 and GY25. General and specific conditions are not separated without structured source data.
Programme content
Study structure
Application and important dates
- Programme or course starts
- Programme or course ends
Salary and salary distribution
Common occupations after graduation
Students
Geographical background
Previous upper-secondary schools and programmes
Completion and outcomes
About the provider
Sources and data quality
Education facts for the selected offering come from Skolverket Susa-navet.
Retrieved . Published . Times are shown in Swedish local time.
Source identity and publication version
- Publication version
- 8e217193-f5fa-4778-b085-a4521fd03e8d
- Education identity in the source
- i.uoh.gu.fym345.11018.20262
- Offering identity in the source
- e.uoh.gu.fym345.11018.20262
- Education-form source code
- HS
- Education code in the source
- FYM345
- Change time according to the source
- 2026-02-16T09:39:01
The provider, education and education offering are separate identities. Application information should be checked on the official website. Supplementary statistics have not been obtained from this source.