Master's program Advanced and Computational MathematicsSpecialization in Computational Mathematics |
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| 1. sem. | Levelling up course | 5 ECTS | ||
| One Basic Course from: Algebraic Geometry, Algebraic Topology, Differential Geometry, Fourier Analysis, Functional Analysis II, Geometric Analysis, Stochastic Analysis, Stochastic Processes |
One Basic Course from: Inverse Problems, Numerical Methods for ODEs, Numerical Methods for PDEs, Numerical Linear Algebra, Numerical Optimization |
One Basic Course from: Introduction to Data Science, Mathematical Foundation of Learning Theory, Mathematical Methods for Uncertainty Quantification, Matrix Methods in Data Science |
24 ECTS | |
| 2. to 3. sem. | 4-5 courses to be chosen out of: | 38 ECTS | ||
Discrete Optimization; Fourier Analysis; Game Theory; Geometric Analysis; Graph Theory; Harmonic Analysis; Hilbert Space Methods; Introduction to Insurance Mathematics and Mathematical Finance; Introduction to Wavelet Theory; Inverse Problems; Mathematical Foundation of Learning Theory; Mathematical Methods for Uncertainty Quantification; Matrix Methods in Data Science; Numerical Methods for ODEs; Numerical Methods for PDEs; Numerical Linear Algebra; Numerical Optimization; Optimization in Machine Learning; Stochastic Analysis; Stochastic Processes; Times Series Analysis; Variational Methods |
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| 3. sem. | Research Seminar or Modelling Seminar | 8 ECTS | ||
| 4. sem. | Master's Thesis | 30 ECTS | ||
| 1. to 3. sem. | Language Courses German (at least level A2), Optional language courses |
15 ECTS | ||
Students with excellent results in their Master's degree qualify for the Ph.D. program. The Ph.D. program places particular importance on developing the ability to conduct self-reliant scientific work. Next to the immersion in the field of specialization, Ph.D. students are encouraged to attend respective lectures and seminars on latest research and actively participate in research group work.