Professor Anders Malthe-Sørenssen
Affiliation: Department of Physics, University of Oslo
My research focuses on developing and applying methods from computational physics to provide a physics-based understanding of systems in physics, materials science, geoscience, and bioscience.
I apply molecular dynamics and statistical physics models to provide fundamental insights into nanoscale processes, friction, geological processes including carbon storage, and biological systems; and I study learning in artificial and biological neural networks simultaneously to develop new, fundamental insights in both AI and neuroscience.
My research is fundamentally cross-disciplinary and I have found that developing long-term research collaborations across disciplines have proven an important avenue for high-impact breakthroughs.
Malthe-Sørenssen received his PhD in Physics from UiO in 1998. He has experience in taking basic research to commercialization, has developed patentable technologies, started companies, successfully raised investor money, and followed the companies to a successful IPO. He has raised more than 35 million EUR to support his research, education and innovation plans.
He was a group leader in Physics of Geological Processes, a Center of Excellence (2003-2013) and is the director of the Center for Computing in Science Education, a Center for Excellence (2016-2026). He has supervised 15 PhD students and has published 5 articles in Nature and 2 in PNAS.
He is a renowned science educator and has written several internationally best-selling textbooks. He is engaged in talent development and is the founder and leader of the Honours-program at UiO and of the CompSci program.
Supervisor for the following CompSci projects
- Bio-inspired neural networks for navigation (available in call 1)
- Large-scale recordings of neurons to reveal mechanisms of learning and memory (available in call 1)
- Artificial and biological neural networks in cognitive tasks (available in call 2)
- Deep networks: artificial vs. biological (available in call 2)
- Computational modelling of neural mechanisms of brain plasticity (available in call 2)
- Mesoscale modelling of plastic instabilities using machine learning (availble in call 2)
- Molecular scale machine-learning based simulations of dynamic fracture in rocks (available in call 2)
Frictional properties of surface structures generated by machine-learning (available in call 1
Machine-learning-based molecular modelling of nanoscale geological processes (available in call 1)
Mesoscale modelling of turbulence and swarming behavior in soft active matter (available in call 1)
Measurement and mechanistic modelling of 3D cell migration (available in call 1)
First-principle simulations of nanoscale geological processes (available in call 2)