NORWAY: 17 PhD positions available within the MSCA COFUND project: CompSci - Natural Sciences

CompSci is a Doctoral Programme launched and managed by the Faculty of Mathematics and Natural Sciences at the University of Oslo (UiO).

CompSci combines a disciplinary doctoral programme with intensive training in computational and data science. It is partly funded by the EU Horizon 2020 under the Marie Skłodowska-Curie Action (MSCA) - Co-funding of Regional, National and International Programmes (COFUND).

The CompSci Doctoral Programme will recruit 32 PhDs in two calls. In this Call, we open for applications to the 2nd cohort of 17 PhD fellowship positions, that will join our PhD programme starting autumn 2022 at UiO.

Application Deadline: 1 February 2022

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

The target groups of the programme are highly talented aspiring researchers with backgrounds in physics, chemistry, bioscience, materials science, mathematics, astronomy or geoscience, who are finishing or have just finished their master degree or have less than four years of full-time equivalent (FTE) research experience.

As a CompSci fellow, you will go through a three-month intensive training in computational and data science with a team of doctoral candidates from across the natural sciences. You will then apply and develop those skills in a research project in your discipline of choice in an internationally leading research group. All CompSci fellows will be enrolled as PhD candidates in one of the departments at the Faculty of Mathematics and Natural Sciences.


Astronomy: Project span computational cosmology and observational and computational studies of the sun

  1. Interpretation of solar observations, using Deep Learning 

    Supervisors: Luc Rouppe van der VoortTiago M. D. Pereira

  2. WholeSun: New codes and frameworks for exascale computing for multi-scale simulations

    Supervisors: Mats CarlssonBoris Gudiksen

Bioscience & AI: Projects focus on computational and experimental neuroscience, addressing the molecular mechanisms of learning and the interplay between biological and artificial learning systems.


  1. Neuron population dynamics and the role of neuromodulators for learning in biological and artificial neural networks

    Supervisors: Marianne FyhnAnders Malthe-Sørenssen

  2. Neural basis of complex memory processing - common challenges in brain and AI

    Supervisors: Marianne FyhnAnders Malthe-Sørenssen

  3. Bio-inspired methods for continual learning in deep neural networks 

    Supervisors: Kai Olav EllefsenMikkel Elle Lepperød

  4. Causal learning in neural networks and the brain 

    Supervisors: Anders Malthe-SørenssenMikkel Elle Lepperød

Chemistry: Computational and experimental projects at the Hylleraas center for computational quantum mechanics and in the science and technology of catalysis.

  1. Ab initio molecular dynamics (MD) for accurate descriptions of entropy and diffusion in nanoporous catalysts

    Supervisors: Stian SvelleMichele Cascella

  2. Development of density-functional methods utilizing tensor densities

    Supervisors: Erik TellgrenTrygve Helgaker

  3. Mechanistic study of CO2 hydrogenation reactions using microkinetic modelling

    Supervisors: Ainara NovaMats Tilset

  4. Molecular noninteracting kinetic energy by machine learning

    Supervisors: Trygve HelgakerSimen Kvaal

  5. Operando studies of porous catalysts and reaction mechanisms

    Supervisors: Stian SvelleSilvia Bordiga

Geoscience: Projects span geophysics, climate research, and geological processes and include traditional computational methods and machine learning based projects.

  1. Mesoscale modelling of plastic instabilities using machine learning

    Supervisors: Luiza AnghelutaAnders Malthe-Sørenssen

  2. Modelling impact of melts on mantle diffusion and viscosity with geodynamic implications

    Supervisors: Razvan CaracasCarmen Gaina

  3. Evaluating mechanisms for intraplate volcanism using the observed distribution of seamounts

    Supervisors: Carmen GainaClinton Phillips Conrad

  4. Molecular scale machine-learning based modeling of dynamic fracture in rocks

    Supervisors: Anders Malthe-SørenssenHenrikAndersen Sveinsson

  5. Optimal Climate

    Supervisors: Joseph Henry LacasceMorten Hjorth-Jensen

  6. Wavelet-based scale estimation of turbulent phenomena in the ocean and atmosphere 

    Supervisors: Pål Erik IsachsenTrude Storelvmo

  7. Predicting laboratory earthquakes using machine learning

    Supervisors: François RenardBenoit Cordonnier

Mathematics & Statistics: Projects span a broad range of mathematical and statistical subjects – all with a focus on machine learning or broader data science methods.

  1. Backtracking gradient descent-based algorithms to defend DNNs against adversarial attacks

    Supervisors: Tuyen Trung Truong 

  2. Deep learning observables of solutions of nonlinear hyperbolic partial differential equations

    Supervisors: Ulrik Skre Fjordholm

Physics: Computational and machine learning projects across several fields of physics spanning quantum mechanics, biological physics and nano science.

  1. Deep-learning based analysis of stem cell differentiation pathways 

    Supervisors: Hanne ScholzDag Kristian Dysthe

  2. Measurement and mechanistic modelling of 3D cell migration 

    Supervisors: Dag Kristian DystheAnders Malthe-Sørenssen

  3. Mesenchymal stem cell differentiation and mineralization in biomimetic hydrogels: microfluidics and modelling

    Supervisors: Dag Kristian DystheLuiza Angheluta

  4. Designing materials for sustainable energy applications using machine learning

    Supervisors: Anders Malthe-SørenssenHenrik Andersen Sveinsson

  5. Development of Quantum Computing Algorithms for studies of quantum mechanical many-body systems

    Supervisors: Morten Hjorth-JensenSimen Kvaal


Formal educational requirements

  • Qualifications (one of the following)

    • A master’s degree or equivalent with a specialization as described for each particular research project

    • A foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system

  • Grades (the norm is as follows)

    • The average grade point for courses included in the Bachelor’s degree must be C or betterin the Norwegian educational system

    • The average grade point for courses included in the Master’s degree must be B or better in the Norwegian educational system

    • The Master’s thesis must have the grade B or better in the Norwegian educational system

  • English language skills

  • Desirable qualifications

    • Good knowledge of the scientific field addressed by the project(s) of interest is desirable

    • Strong quantitative and analytical skills are an advantage

    • Good communication and interpersonal skills are necessary

Candidates without a Master’s degree have until June 30, 2022 to complete the final exam.

Read more about the formal requirements.

Research experience

As per Marie Sklodowska-Curie Action requirement, the candidate must at the date of the programme call deadline be within the first four years (FTE-equivalent) of their research careers and not yet be awarded at doctoral degree. Full-time equivalent research experience is measured from the date when you obtained the degree entitling you to embark on a doctorate (e.g. from your Master's degree).

Mobility requirement

As per Marie Sklodowska-Curie Action requirement, the candidate may not have resided and/or carried out their main activity in Norway for more than 12 months in the 3 years immediately before the date of recruitment. Researchers with refugee status, as defined by the Geneva Convention, benefit from a less restrictive mobility rule: the refugee procedure (i.e. before refugee status is conferred) will not be counted as a period of residence/activity in Norway. The secondment host country is exempt from the mobility rule.

Employment conditions

  • 3-year full time employment contract

  • Full social security and benefits

  • Attractive salary according to your qualifications and in a position as PhD Research fellow

Employment conditions also include

  • Attractive welfare benefits such as paid sick leave, parental leave and the right to 5 weeks holiday, and a generous pension agreement

  • Access to special career development programmes, and research schools

  • Health, safety and environment policies, procedures and tools that safeguard your workplace

  • Co-determination at work place through trade unions and safety representatives

  • Excellent research environments with highly competent and motivated researchers

  • Good working conditions and high standard facilities and offices