Why study Computational Science?
Modern scientists increasingly rely on computational modeling and data analysis to explore and understand the natural world. Given the ubiquitous use in science and its critical importance to the future of science and engineering, computational modeling plays a central role in progress and scientific developments in the 21st Century.
This programme aims at educating the next generation of cross-disciplinary science students with the knowledge, skills, and values needed to pose and solve current and new scientific, technological and societal challenges.
A programme with a large range of topics
It is a unique educational programme that treats in a comprehensive way computation as the triple junction of algorithm development and analysis, high performance computing, and applications to scientific and engineering modeling and data science.
All disciplines in the Sciences are represented in this programme and you can thereby explore and design thesis projects that cover a large range of topics and own interests, from Mathematics and Computational Science to the Physical Sciences and Life Sciences.
New methods that make challenging problems tractable
Scientific computing focuses on the development of predictive computer models of the world around us. As studies of physical phenomena evolved to address increasingly complex systems, traditional experimentation is often infeasible. Computational modeling has become a primary tool for understanding these systems; equal in stature, for the right questions, to analysis and experiment. The discipline of scientific computing is the development of new methods that make challenging problems tractable on modern computing platforms, providing scientists and engineers with new windows into the world around us.
Develop tools to find trends within datasets
Data science focuses on the development of tools designed to find trends within datasets that help scientists who are challenged with massive amounts of data to assess key relations within those datasets. These key relations provide hooks that allow scientists to identify models which, in turn, facilitate making accurate predictions in complex systems.
For example, a key data science goal on the biological side would be better care for patients (e.g., personalized medicine). Given a patient’s genetic makeup, the proper data-driven model would identify the most effective treatment for that patient.
Change the future of computer simulations
Combining machine learning and data analysis with quantum computing is an exciting topic, which can completely change the future of computer simulations, and the way we study physical systems at the smallest length scales.
Combining insights with mathematical tools and computational skills
An important aim of this programme is to develop your abilities to pose and solve problems that combine insights from one or more disciplines from the natural sciences with mathematical tools and computational skills. This provides a unique combination of applied and theoretical knowledge and skills.
These features are invaluable in today's multi-disciplinary environments, both academic and professional. The main focus is not to educate computer specialists, but to give you an education with a solid understanding in basic science as well as an integrated knowledge on how to use essential methods from computational science.
This requires an education that covers both the specific disciplines like physics, biology, geoscience, mathematics etc with a strong background in computational science.
Ignite new transformational connections in research and education
This Master of Science programme is unique in the sense that it will enable application-driven computational modeling while also exposing disciplinary computational scientists to advanced tools and techniques. This will hopefully ignite new transformational connections in research and education.
The programme has the following options
- Computational Science: Applied Mathematics and Risk Analysis
- Computational Science: Astrophysics
- Computational Science: Bioinformatics
- Computational Science: Bioscience
- Computational Science: Chemistry
- Computational Science: Geoscience
- Computational Science: Imaging and Biomedical Computing
- Computational Science: Materials Science
- Computational Science: Mechanics
- Computational Science: Physics
This is a new programme at the University of Oslo and through various activities, spanning from common meetings and field trips to various social gatherings, we will gradually build up a top learning environment where you will thrive as a student and learn to develop your scientific creativity through exciting thesis topics.
The University of Oslo offers a rich and active student environment with more than 200 student led activities and organizations.
Studies abroad at top universities in Canada and the USA, Asia and Europe
The involved researchers have extensive collaborations with other researchers worldwide. The exchange possibilities range from top universities in Canada and in the USA, in Asia and in Europe as well as leading National Laboratories in the USA. Studies at other institutions can be planned from the very first semester of the programme, although we normally recommend planning a stay abroad from the second semester or later semesters. Read more about studies abroad.
A significant aspect of this programme is the ability to offer new educational opportunities that are aligned with the needs of a 21st century workforce.
Many companies are seeking individuals who have knowledge of both a specific discipline and computational modeling. Read more about career prospects.