Master program in Computational Science at the University of Oslo

Go to https://github.com/ComputationalScienceUiO/ComputationalScience

You will receive an email later with the link together with weekly updates with weekly updates about the program as well. The various sessions during the start-up week will all be recorded and available from the above website.

The schedule for the first week can be found at (example for CS:Physics and CS:Materials Science) https://www.mn.uio.no/fysikk/studier/master/studiestart/index.html

The consulting company McKinsey recently released a report arguing that the need for computational and data scientists – that is, workers who can successfully analyze, model, and interpret data, and use it to inform critical business decisions - is going to grow rapidly. A wide range of industries now use computational modeling and data analytics to inform many aspects of their day-to-day practices, encompassing a range of activities that include product design, optimizing manufacturing processes, hiring decisions, and choosing advertising targets. The trend is clear, as are the implications for new employees – being fluent in the tools used to work with data and computational models, as well as to be intelligent consumers of the products of these tools, will be a critical and highly demanded skillset.

• By 2020, it is expected that one of every two jobs in the STEM fields will be in computing (Association for Computing Machinery, 2013). Note the year of 2013. Today we can safely say almost all jobs!
• Computation is an essential and cross-cutting element of all S(cience)T(echnology)E(ngineering)M(mathematics) disciplines
• Computational science has developed into a discipline of its own right
• Computations and the understanding of large data sets will play an even larger role in basically all disciplines of STEM fields, Medicine, the Social Sciences, the Humanities and education
• Students at both undergraduate and graduate level are unprepared to use computational modeling, data science, and high performance computing – skills valued by a very broad range of employers.
• The 3rd Industrial Revolution will alter significantly the demands on the workforce. To adapt a highly-qualified workforce to coming challenges requires strong fundamental bases in STEM fields. Computational Science can provide such a background at all stages.

The program is a collaboration between seven departments and classical disciplines:

• Institute of Theoretical Astrophysics
• Department of Biosciences
• Department of Chemistry
• Department of Geoscience
• Department of Informatics
• Department of Mathematics
• Department of Physics

The program is multi-disciplinary and everbody who has completed undergraduate studies in science and engineering, with a sufficient quantitative background, is eligible. The language of instruction is Norwegian or English.

Numerical simulations of various systems in science are central to our basic understanding of nature and technlogy. The increase in computational power, improved algorithms for solving problems in science as well as access to high-performance facilities, allow researchers nowadays to study complicated systems across many length and energy scales. Applications span from studying quantum physical systems in nanotechnology and the characteristics of new materials or subamotic physics at its smallest length scale, to simulating galaxies and the evolution of the universe. In between, simulations are key to understanding cancer treatment and how the brain works, predicting climate changes and this week's weather, simulating natural disasters, semi-conductor devices, quantum computers, as well as assessing risk in the insurance and financial industry. These are just a few topics already well covered at the University of Oslo and that can be topics for coming thesis projects as well as research directions.

Computing means solving scientific problems using computers. It covers numerical as well as symbolic computing. Computing is also about developing an understanding of the scientific process by enhancing algorithmic thinking when solving problems. Computing competence has always been a central part of the science and engineering education.

Modern computing competence is about

• derivation, validation verification, and implementation of algorithms
• understanding what can go wrong with algorithms
• overview of important, known algorithms
• understanding how algorithms are used to solve mathematical problems
• reproducible science and ethics
• algorithmic thinking for gaining deeper insights about scientific problems

The power of the scientific method lies in identifying a given problem as a special case of an abstract class of problems, identifying general solution methods for this class of problems, and applying a general method to the specific problem (applying means, in the case of computing, calculations by pen and paper, symbolic computing, or numerical computing by ready-made and/or self-written software). This generic view on problems and methods is particularly important for understanding how to apply available, generic software to solve a particular problem.

Computing competence represents a central element in scientific problem solving, from basic education and research to essentially almost all advanced problems in modern societies. Computing competence is simply central to further progress. It enlarges the body of tools available to students and scientists beyond classical tools and allows for a more generic handling of problems. Focusing on algorithmic aspects results in deeper insights about scientific problems.

In this program you learn to use the computer as a laboratory for solving problems in science and engineering. The program offers exciting thesis projects from many disciplines: biology and life science, chemistry, mathematics, informatics, physics, geophysics, mechanics, geology, computational finance, computational informatics, b ig data analysis, digital signal processing and image analysis – you select research field according to your interests.

A Master’s degree from this program gives you a methodical training in planning, conducting, and reporting large research projects, often together with other students and university teachers. The projects emphasize finding practical solutions, developing an intuitive understanding of the science and the scientific methods needed to solve complicated problems, use of many tools, and not least developing own creativity and independent thinking. The thesis work is a scientific project where you learn to tackle a scientific problem in a professional manner. The program aims also at developing a deep understanding of the role of computing in solving modern scientific problems.

From this program you gain deep insights in the fundamnetal role computations play in our advancement of science and technology, as well as the role computations play in society.

While discipline-based master's programs tend to introduce very strict requirements for specific courses, we believe in adapting a computational thesis topic to the student's background, thereby opening up for students with a wide range of bachelor's degrees. A very heterogeneous student community is thought to be a strength and unique feature of this program.

• Computational Science: Applied Mathematics and Risk Analysis
• Computational Science: Astrophysics
• Computational Science: Bioinformatics
• Computational Science: Biology
• Computational Science: Chemistry
• Computational Science: Geoscience
• Computational Science: Imaging and Biomedical Computing
• Computational Science: Materials science
• Computational Science: Mechanics
• Computational Science: Physics

The thesis projects will be tailored to your needs, wishes and scientific background. The projects can easily incorporate topics from more than one discipline.

In order to build a common study program and identity as a Computational Science student, there will be two compulsory courses that aim at providing topics of common and broad interest.

• FYS-STK3155/4155: Applied Data analysis and machine learning, 10 ECTS, Fall semester
• MAT4100: Introduction to numerical analysis, 10 ECTS, Fall semester
• IN3200/4200: High-Performance Computing with Numerical projects, 10 ECTS, spring semester

Candidates who are capable of modeling and understanding complicated systems in natural science, are in short supply in society. The computational methods and approaches to scientific problems students learn when working on their thesis projects are very similar to the methods they will use in later stages of their careers. To handle large numerical projects demands structured thinking and good analytical skills and a thorough understanding of the problems to be solved. This knowledge makes the students unique on the labor market.

Career opportunities are many, from research institutes, universities and university colleges and a multitude of companies. Examples include IBM, Hydro, Statoil, and Telenor. The program gives an excellent background for further studies, with a PhD as one possible goal.

The program has also a strong international element which allows students to gain important experience from international collaborations in science, with the opportunity to spend parts of the time spent on thesis work at research institutions abroad.

The goal of CCSE is to integrate computing as a natural tool in basic educations, to make the education research near and to prepare students for an interdisciplinary workplace.

The center will function as our coordintaing unit for common scientific and social activities.

Location: fourth floor, eastern wing of the Physics building.