Specialized Master in Computational Science
The rapid development of computational methods and the ever-increasing power of modern computers have fundamentally transformed how scientific problems are approached. Today, many of the most exciting challenges in the natural sciences are tackled through a combination of scientific insight, mathematical modeling, and advanced computation.
The Master’s program in Computational Science (90 ECTS credits Mono or 90 ECTS Major with a 30 ECTS Minor) is designed for students with a background in the natural sciences or computer science who want to work at this interface. It equips students with the skills to formulate, analyze, and solve complex scientific problems using modern numerical, algorithmic, and data-driven methods. Core courses provide a strong foundation in simulation techniques, scientific computing, and data analysis, while emphasizing algorithmic thinking and efficient implementation.
A key feature of the program is its strong practical orientation: students gain hands-on experience in coding, software development, and scientific project work, preparing them to translate theoretical concepts into working computational tools. Through a wide range of elective courses, students can further specialize methodologically or deepen their expertise in application areas such as astrophysics, physics, chemistry, geosciences, and related fields.
By combining methodological rigor with application-driven learning, the program prepares graduates to contribute to cutting-edge research in academia and to meet the growing demand in industry for experts who can bridge the gap between scientific inquiry and advanced computation.
*** We do not compensate foreign or local MSc students ***
*** This is the Master program in Computational Science. For the Master program in Computer Science (aka Informatics), see here ***
Program structure
The Specialized Master in Computational Science has a core that focuses on general principles (Methodological Foundations, Numerical Methods for Differential Equations in Simulations, Advanced High-performance Computing, Methods for Visualizing Simulation Data, Machine Learning in Science). The core courses all have a strong practical component in code development and programming. In addition, there are electives with a focus on the hands-on aspect. Possible directions include Computational Physics and Astrophysics, Computational Chemistry, Computer Graphics, Applications in Earth and Environmental Sciences.
| Compulsory Modules (Spring 2027 onwards) | Semester | ECTS credits |
| ESC 406: Computational Thinking II | Spring | 2 |
| ESC 411: Individual seminar work in Computational Science | Fall | 5 |
| ESC 412: Advanced High Performance Computing (see note 1 below) | Spring | 5 |
| ESC 413: Computational Thinking I | Fall | 2 |
| ESC 414: Practicum in advanced simulation science | Any | 10 |
| ESC 500: Master thesis | Any | 30 |
| 10SMSTS-200: Interdisciplinary Introduction to Machine Learning - Exercises | Any | 2 |
| 10SMSTS-201: Interdisciplinary Introduction to Machine Learning - Theory | Any | 3 |
| Compulsory Modules (before Spring 2027) | ||
| ESC 203: Advanced Simulations in the Natural Sciences | Spring | 5 |
| ESC 406: Computational Thinking II | Spring | 2 |
| ESC 411: Individual seminar work in Computational Science | Fall | 5 |
| ESC 412: Advanced High Performance Computing (see note 1 below) | Spring | 5 |
| ESC 413: Computational Thinking I | Fall | 2 |
| ESC 500: Master thesis | Any | 30 |
| PHY 371: Machine Learning for the Sciences | Spring | 5 |
|
Elective Modules |
||
| ESC 204: Computational Methods for Radiative Transfer | Spring | 5 |
| ESC 415: Internship in Computational Science | Any | 6 |
| ESC 481: Summer School Computational Science | Any | 1 |
| ESC 802: Academia Industry Modelling (AIM) week | Fall | 2 |
| AST 245 or AST 246: Computational Astrophysics | Fall | 6 or 10 |
| MAT 681: Coding Theory Meets Molecular Biology | Fall | 5 |
| MAT 827: Numerical Methods for Hyperbolic Partial Differential Equations | Spring | 10 |
| STA 426: Statistical Analysis of High- Throughput Genomic and Transcriptomic Data | Fall | 5 |
| GEO 442: Remote Sensing Theory: Understanding signals | Fall | 4 |
| GEO 877: Spatial Algorithms | Spring | 3 |
| CHE437: Surface and Interface Science | Fall | 4 |
| CHE 748: Introduction to Computational Materials Science and Quantum Chemistry | Spring | 2 |
| M3L 521: AI4Good (see note 2 below) | Fall | 6 |
| 03SM22MIS010: Advanced Software Engineering (see note 2 below) | Spring | 3 |
| 03SM22BMI007: Computer Graphics | Spring | 6 |
|
03SM22MI0057: Applied Computer Graphics |
Fall | 3 |
|
101-0692-00L: Selected Topics in Computational Mechanics (ETHZ) |
Fall | 3 |
|
401-4944-20L: Mathematics of Data Science (at ETHZ) |
Fall | 8 |
Admission requirements
Admission to the specialized Master's program is by application.
The specialized mono/major study program Computational Science (note: not Computer Science) at Master's level requires a Bachelor of Science degree with:
- A major in a natural science (e.g., Astrophysics, Biology, Chemistry, Geography, Physics) with a minor in Computational Science or Computer science (Informatics); or
- A major in a computational field with a minor in a natural science or Computational Science.
Interest in both computing and natural science are prerequisites for applying. Please note that having 1st year introductory courses in the natural sciences (listed above) is not considered sufficient.
In certain cases, students will be required to additionally take courses to fulfil the entry requirements.