Master's programme in Computer Simulations for Science and Engineering
Computer Simulations for Science and Engineering (COSSE) is a master’s programme within the multidisciplinary field of Computational Science and Engineering (CSE), which is an enabling technology for scientific discovery and engineering design. CSE involves mathematical modelling, numerical analysis, computer science, high-performance computing and visualisation. The remarkable development of large-scale computing in recent decades has turned CSE into the “third pillar” of science, complementing theory and experiment.
Computer Simulations for Science and Engineering at KTH, TU Berlin and TU Delft
The master's programme in Computer Simulations for Science and Engineering is a two-year programme including compulsory mobility for the students. The programme is given jointly by KTH, Technical University of Berlin in Germany and Delft University of Technology in the Netherlands. Students will enter one of the universities and continue in their second year at one of the other universities. As a general rule, students will be admitted to TU Berlin for the first year.
The programme includes three semesters of courses followed by a fourth research semester spent on a master’s degree project under the supervision of both universities attended. The students receive two degrees from both the first and second year universities.
Students in the master's programme in Computer Simulations for Science and Engineering will become very familiar with the tools of CSE, which includes:
- mathematical modelling techniques
- simulation techniques (discretisations, algorithms, data structures, software in CSE)
- analysis techniques (data mining, data management, visualisation)
The students will become experts in the generic tools as applied to one of the specialisations offered by the universities. Upon graduation, the students will have acquired
- familiarity with selected scientific and engineering application areas and their mathematical models.
- a knowledge base and skills ranging from formulation of a mathematical model to constructing software for high-performance computer architectures.
- effective communication skills for interacting on written and oral presentations with the professional community as well as management and society at large..
- a degree of independent and critical appraisal of the capability and limitations of, and results produced by, computational modelling.
After the beginning of the second semester, students, scholars and teachers of all partner universities will meet for a joint workshop to advise students on their elective courses, including possible topics for their master’s thesis. The workshop will include joint research and cultural activities and provide contacts to research departments in academia and industry.
The master's degree project comprises 30 ECTS credits, and is carried out in the second half of the second year of the programme. Students are supervised and examined by faculty staff from both the home and the host university. The project work may be performed in a research group at the institute or as a project in industry or a consultancy company.
As to career opportunities, a degree from the master's programme in Computer Simulations for Science and Engineering opens several opportunities:
- to join the international research community by continuing PhD studies in the CSE and science/engineering fields where simulation and high-performance computing is applied: fluid dynamics, electromagnetics, molecular physics, quantum chemistry, material science, chemical engineering, structural mechanics, biocomputing, and many more.
- to gain employment in “end-user” high-tech industry and “provider companies”, using and developing tools for advanced computer simulation in the pharmaceutical, automotive, materials, aeronautics, power generation, micro-electronics, etc. industries.
- to start new innovative companies which are based on CSE expertise.
The programme is highly research oriented. So far, roughly two thirds of the graduates have continued with a PhD position at the consortium partners KTH, TU Berlin, and TU Delft as well as at leading international universities, for example, MIT (USA), Oxford University (UK), Purdue University (USA), Uppsala University (Sweden), ETH (Switzerland), Karolinska Institute (Sweden), University Melbourne (Australia), INRIA (France), Simon Fraser University (Canada), UPC Barcelona (Spain), NTU Singapore, and many others.
The graduates of this programme are in high demand on the labour market as well. Alumni work in large and smaller companies such as Ericsson (Sweden), IBM Peking (China), BASF (Germany), Tata Steel (The Netherlands), DeCode genetics (Iceland), Sabic (India), AT Kearney (The Netherlands), HERE (Germany), TNO (The Netherlands) and many others.
Find out what students from the programme think about their time at KTH.
Faculty and research
The programme is run by the Department of Mathematics. The Department of Mathematics at KTH hosts some of the strongest Swedish research groups in mathematics. It comprises four units: Mathematics, Mathematical Statistics, Optimisation and Systems Theory, and Numerical Analysis. Jointly, these units carry out research in a broad spectrum of mathematical disciplines, ranging from pure to applied mathematics. Some of the current larger research centres hosted at the department are:
- Random matrices, sponsored by the Wallenberg foundation
- Image processing, sponsored by SSF
- PDE, sponsored by the ERC/VR/Gustafsson’s foundation
- MathDataLab, sponsored by Brummer & Partners
The research at the Division of Numerical Analysis includes numerical methods for stochastic and deterministic differential equations, computational modelling in systems biology, numerical methods for micro and complex flow, multiscale methods, finite element methods for multiphase flow. The researchers are working actively in many interdisciplinary corperative partnerships, e.g., the Swedish e-Science Research Centre (SeRC), the Linné FLOW Centre, and with Karolinska Institutet. Students will also have access to Sweden’s fastest supercomputers through the PDC Centre for High-Performance Computing. The faculty members Professor Anders Szepessy and Professor Anna-Karin Tornberg are members of the Royal Swedish Academy of Sciences.
The programme is run at the Institute of Mathematics of Faculty II – Mathematics and Natural Sciences is an application-oriented research and teaching institute of Technische Universität Berlin. Our Institute offers courses in mathematics, mathematical economics, technomathematics (both as a bachelor and master’s) and scientific computingas well as extensive mathematics services for students attending other courses.
The Institute of Mathematics at Technische Universität Berlin distinguishes itself through its diverse corperative partnerships within Berlin, nationwide and internationally. Hosting and participating in mathematical research training groups DFG, Deutsche Forschungsgemeinschaft, in DFG collaborative research centres and in DFG research units, acquisition of ERC grants as well as various prices and awards which emphasise the high level and international reputation of the institute.
The programme is run in close collaboration with the Berlin Mathematical School (BMS), a joint graduate school of the mathematics institutes of the three major Berlin universities. BMS is based at the Institute of Mathematics at TU Berlin, being funded within the framework of the German “Initiative for Excellence”.
The Institute is strongly involved in the Research Center Matheon “Mathematics for Key Technologies”, an internationally renowned Centre of Excellence run jointly by TU Berlin, Free University, Humboldt University, Zuse Institute, and Weierstrass Institute, in which nearly 200 scientists pursue application-oriented and theoretical research.
The mostly application-oriented research at the Institute of Mathematics is based on the orientation of the existing workgroups:
- Discrete and Algorithmic Mathematics
- Stochastics and Financial Mathematics
- Geometry and mathematical Physics
- Modelling, Numerics, Differential Equations
The departments research is thereby characterised by a plurality of third-party-funded projects, especially within the network of Mathematics at Berlin and in cooperation with other natural and engineering sciences. With its research accomplishments, the Department for Mathematics earns leading positions in national and international rankings.
The faculty member Professor Volker Mehrmann is a member of the National Academy of Science and Engineering (acatech).
The programme is a special track of the Applied Mathematics master’s programme run by the Delft Institute of Applied Mathematics (DIAM). DIAM is the largest mathematics research and education group in The Netherlands. DIAM consists of 6 sections: Numerical Analysis, Mathematical Physics, Analysis, Applied Probability, Statistics and Optimization. Within these sections a broad range of topics is covered from applied to pure mathematics. The research in DIAM is also embedded in the following larger initiatives:
- TU Delft Institute for Computational Science and Engineering
- Multi-Objective design Optimization of fluid energy machines (supported by the EU-programme Horizon2020)
The faculty includes Professor Cornelis Oosterlee and Professor Arnold Heeminck.
In the numerical analysis group, the main research directions are: discretisation of partial differential equations, fast and robust (non) linear solvers and High-Performance Computing. With respect to solvers the TU Delft group is one of the leaders in the field. Some current research topics include: fast solvers for seismic research, computational finance, medical imaging, computational health, Computational Fluid Dynamics, High Performance Computing, Power Grid simulations, and Computational Physics.
In Mathematical Physics, research is done on modelling of various physical phenomena and a combination of these models with High Performance Computing. Research topics are: data assimilation, simulation of transport processes, high-performance computing, differential equations and asymptotics, and stochastic differential equations.
Within the Optimisation Section, the following topics are covered: mixed integer linear programming, convex geometry and optimisation, combinatorial optimisation, semidefinite programming, discrete and computational geometry, geometry of lattices, and algorithms and complexity.
Changes in the programme may occur.