Training programme

The CompSci programme will train a new generation of natural science researchers with disciplinary, interdisciplinary and transferable skills and a foundation in computational methods. This will provide them with the knowledge, skills and vision to digitally transform the European education, research, government and industry sectors.

Researchers need skills in computing and data science

The use of computing and data science fundamentally changes our research, industry, government and society. To be competitive in research and innovation, as an aspiring young researcher, you will need to acquire a solid set of skills in computing and data science, in addition to your disciplinary basis.

The goal of this training programme is to include these skills in a wide array of research disciplines. Traditionally, the training of computer science experts has been difficult to directly transfer and apply into a disciplinary setting. The CompSci programme will ensure this integration and help form a new cohort of future interdisciplinary scientists.

listing of main objectives

Transform your discipline through computing and data science

For example, an experimental bioscientist or a materials scientist must learn computing and data science with methods and examples that are adapted to their field of research in order for them to be able to apply these methods effectively in their field.

We believe this will provide you a combination of competences in a discipline and in digital skills needed to impact research and education in your discipline, bring a new cross-disciplinary approach to research and innovation, and help digitally transform academia and industry. It will also provide you as a young researcher with skills and competence that answer the call for a digitally competent workforce and improve your employability.

Participating in a cross-disciplinary team of doctoral fellows

In this programme you will join a team of other aspiring researchers from across the fields of science.

As a team you will first go through a three months initial intensive training in scientific programming, computational science and data science. This will help you bond with doctoral fellows in your cohort and help you build a network both within and across disciplines. 

Research training in disciplinary research groups

You will then apply and develop these skills in an individual research project in your discipline – mathematics, bioscience, geoscience, chemistry, materials science, astronomy or physics – in research groups at the University of Oslo.

The research groups and supervisors are experienced supervisors on a top international level, have a track record in interdisciplinary research, and integrate computational and disciplinary approaches.

Overview of the training programme

The PhD programme at the Faculty of Mathematics and Natural Sciences is a three-year programme, which includes 30 ECTS of courses. As part of the CompSci programme you will start with a three-month intensive training programme in computing and data science.

You will take the PhD-level courses FYS4150 Computational science (10 ECTS) and FYS-STK4155 Applied data analysis and machine learning (10 ECTS). These courses are project driven and both the teaching and the projects may be adapted to your background and research ambitions.

You will take the course together with the 15 other doctoral candidates in the CompSci programme, and you will have a dedicated learning assistant for this group. We will provide an initial workshop for candidates who do not have any background in scientific programming.

In addition, the programme consists of yearly week-long workshops that focus on advanced computing and data science skills as well as other transferable skills.

illustration of the training program

Overview of computational courses and annual workshops

Computational modeling (FYS4150) Data analysis and machine learning (FYS-STK4155)
Numerical methods from the sciences: numerical solution of differential equation, numerical linear algebra, interpolation, numerical integration, Monte-Carlo methods, algorithmic modeling, modeling of data, visualization (C++ or Python) Bayesian statistics and learning, common distributions, central algorithms in data analysis and machine learning, (Monte Carlo, Markov chains, Gibbs samples, data optimization, regression, neural networks, decision trees), visualization

In addition to FYS-STK4155 and FYS4150, you will take a mandatory course in Science, Ethics, and Society (MNSES9100).

The remaining 5 ECTS of the 30 ECTS training programme in the PhD programme will be covered by the yearly workshops.

Large computing projects and hpc computing / Written communication / Intro to project management Data structures and large data sets / Oral communication / Open science & data sharing, RRI, gender bias Professional coding and open source / Entrepreneurship and IPR/ CV writing, interview training, grant writing
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