Master Scientific Computing
Scientific Computing is the challenging combination of applied mathematics and computer science to solve application problems by means of numerical methods.
The program, run by the Faculty for Mathematics and Computer Science in close collaboration with the Interdisciplinary Center for Scientific Computing (IWR) teaches students both the theoretical concepts of computer-based mathematical modelling and the practical aspects of realising complex algorithms in scientific software. By studying an application area as a minor subject, the education is focussed on real-world problems and the interdisciplinary communication that shapes the modern world of research & development.
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Examinations and Credits Mathematics
The Office for Examinations and Credits Mathematics administers examination results in the bachelor's and master's programs in mathematics, including “Mathematics of Machine Learning and Data Science”, and the master's program in “Scientific Computing”. The Office also issues transcripts and certificates of academic achievements.
Facts and Figures
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Degree | Master of Science (M. Sc.) |
Type of Study | Consecutive |
Beginning of Program | Winter and summer semester |
Standard Period of Study | 4 semesters |
Languages | English, some German |
Application Procedure | Consecutive master's program with access restriction |
Study Plan
The first year consists of courses in the three areas of this master program: Mathematics, Computer Science and a Field of Application. In the second year students prepare and conduct their research project. This phase is started with two specialization lectures and leads to a master project. To this end, students should choose a combination of courses in terms 1, 2 and 3 leading to a specialization within the master course.
Program Overview
Mathematical methods taught in this master program include:
- Numerical methods for ODE and PDE
- Statistics and data analysis
- Differential geometry and computer algebra
- Linear and non-linear optimization methods
- Computational methods in fluid dynamics
Computer Science methods list for example:
- Parallel computing
- Scientific visualization
- Mixed-integer programming
- Spatial databases
- Image processing techniques
Applications for Scientific Computing come from:
- Physics and Astronomy
- Robotics
- Weather and Climate Modelling
- Text and Data Mining
- Theoretical Chemistry
- Biology
- Scientific Visualization
- Economics
- Social Sciences
- Cultural Heritage
The program is linked with HGS MathComp, the doctoral school for mathematical and computational modeling at Heidelberg University. Top students in the first year course will get an invitation to join the doctoral school already for the second master year, opening the possibility to a direct integration into the HGS MathComp PhD program, a master course directly leading to a PhD project (research oriented master track).
Forms: Master Thesis and Specialization Area
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Degree Regulations, Admission Regulations, and Course Handbook (Current)
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Older admission regulations, degree regulations, and course handbooks can be found in the download center.