Rebecka Jörnsten             

Professor of Biostatistics and Applied Statistics

Division of Applied Mathematics and Statistics

Mathematical Sciences
University of Gothenburg/ Chalmers

Vice-Dean for Research and Research Infrastructures
Faculty of Science, University of Gothenburg

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What's new?

Postdoc opening in AI and Machine Learning

Start date fall 2022.

This position is funded through the two largest research programs in Sweden, the Wallenberg AI, Autonomous Systems and Software Program (WASP) and and the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS).

Project: "Massively parallel in vivo gene editing and AI modeling to decipher brain tumor invasion" is in collaboration with Sven Nelander's group at Uppsala. This postdoc project centers on the development of new neural network methodologies for modeling noisy, complex biological data to discover biological signatures of tumor invasion. You would be working with a postdoc in the Nelander lab on an innovative set of experiments that will be conducted to collect information on different types of invasion. Here, at Chalmers, your focus would be on methods development.
Link to the application system.


My research centers on the development of new statistical methodology for neural networks, network modeling, clustering and model selection, with applications to high-dimensional biological data.

My group consists of 3 PhD students and 1 postdoc. We work on projects ranging from multi-group and multi-view data integration, multilayer network modeling, interpretable deep neural networks and analysis challenges in systems biology. For more details, please check the List of Current Projects below - we are always looking for master students who want to get involved in research projects.

Data integration and joint modeling are a rich source for research problems. These types of problems are central components in several joint projects in collaboration with Sven Nelander's group. Our group aims to formulate integrated models for mRNA, microRNA, DNA copy number, methylation and mutation in human cancer. These projects are supported by two grants from the Swedish Foundation for Strategic Research as well as the Wallenberg AI, Autonomous Systems and Software Program (WASP) and the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS).
We also collaborate with Mika Gustafsson's group at Linköping university on data integration tasks, with a special emphasis on the utilization of prior biological information. This project is also funded through WASP and DDLS.

I collaborate with Johan Jonasson on new regularization techniques for deep neural networks to better handle the presence of mislabeled data. This project is supported by the Wallenberg foundation

In a joint research project with Professor Giuseppe Durisi , Chalmers Electrical Engineering, we investigate the generalization properties of deep neural networks - how come such over-parameterized models perform so well on test data? The project "Generalization bounds for Deep Neural Networks: Insight and Design" is funded through the WASP Graduate School .

Together with Balazs Kulcsar at Electrical Engineering, we work on research problems merging control theory and neural networks. Centiro, have funded two industry PhD positions on this topic.

Since 2018, I am the Vice-Dean for Research and Research Infrastructures at Faculty of Science , University of Gothenburg. I chair the Faculty Board's advisory committee, where we discuss interdepartmental activities, review sabbatical applications and Faculty prizes and awards. We also organize faculty events.

List of Current Projects

We are always looking for master students who want to be a part of our research team. Please contact me if any of the projects sound interesting to you. You are also welcome to contact me with your own project idea. I usually supervise 5-7 master students each year.

AI, Deep Learning

  • Neural Networks and Control Theory: Together with Professor Balazs Kulcsar at Electrical Engineering, I supervise PhD students Viktor Andersson and Vincent Szolnoky, funded by Centiro . In this team we work on problems ranging from methodological and theoretical challenges in neural networks to practical applications in logistics and transportation.
  • AI-Integromics: We have several theses projects centering on the development of data integration methods for systems biology using deep neural networks paired with structured regularization. 2 postdocs in this group work in this area in collaboration with Mika Gustafsson's group at Linköping university and Sven Nelander's group at Uppsala University.
  • Machine Learning, Systems Biology

  • Flexible Joint Matrix Factorization: My PhD student Felix Held is working on both theoretical and practical aspects of data integration through matrix factorization. There are many open problems one can pursue here; scalability, applications, generalizations.
  • Accelerated training and regularization of neural networks: My PhD students Oskar Allerbo and Maria Matveev work on methodological projects centering on new regularization techniques and training strategies for neural networks.
  • Teaching

    Teaching Philosophy

    My classes are usually made up of a mix of students; undergraduates, master students and PhD students and all from different fields of study. I enjoy this kind of dynamic classroom.
    I tend to mix black-board lectures with computer demonstrations for all my classes. My goal in teaching is that the students leave my class recognizing that statistical modeling is not a "push-the-button" type exercise, and every data set requires unique consideration.

    Courses & Workshops

    In past years I have cycled teaching applied statistics couses (Linear models, Applied multivariate analysis) and method courses (Statistical inference principles and Survival analysis).
    My main focus for teaching now is Statistical Learning for Big Data and master thesis projects.
    Here are some links to recent courses:


    • Jonatan Kallus 2014-2020 Integrative modeling of cancer.
    • Jose Sanchez, 2009-2014 ​Network models with applications to genomic data: generalization, validation and uncertainty assessment Currently working as an analyst at Astra Zeneca
    • Alexandra Jauhiainen 2009-2010 Statistics in Gene Expression, Metabolomics, and Comparative Genomics in Evolution (co-supervisor). Currently working as principal statistician at Astra Zeneca
      Current Master students
    • Maria Matveev Accelerated training of neural networks using coordinated learning
    • Anton Eliasson Gustafsson and Kayed Mahra. Modeling delays in maritime transport, project with Centiro.
    • Gustav Johannesson and Alexander Lindhardt Machine Learning Based Charging Decision Policy for a Fleet of Electric Vehicles , project with Volvo
    • 2021: Only examiner commitments due to faculty administration
    • 2020: Selma Tabakovic, Oskar Liew, Per Ed.
    • 2019: Jacob Söderström, Isak Hjortgren, Sandra Larsson, Jacob Lindbäck, Mikael Böörs, Elijah Ferreira, Arash Shahsavari, Sandra Vikander, Linn Engström, Andreas Syren, Viktor Andersson, Natalja Vessilinova, Elijah Ferrera, Wade Rosco
    • 2018: Olof Ekborg, Philip Arndt, Fredrik Beiron, Johan Björk, Adam Brinkman, Fredrik Kjernald, Karl Svensson.
    • 2017: Andrew Nisbet, Leif Schelin, Alex Bergkvist, Sebastian Franzen, Filip Birve
    • 2016: Björn Hedner, Nils Wireklint, Emilio Jorge, Pasha Hashemi, Ludvig Vikström, Oskar Lilja, Maja Fahlen
    • 2015: Sofia Hjalmarsson, Sebastian Anerud, Susanne Pettersson, Linus Lundin
    • 2002-2014: Johanna Svensson, Patrik Johansson, Viktor Skokic, Johanna Sigmundsdottir, Tobias Abenius, Eric Burlow, Owen Martin, Diane Richardson