Sort by
Refine Your Search
-
PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
, computation, and experiments to model and manipulate the physical forces experienced by invading cancer cells. The overarching goal is to identify biomechanical “weak points” in cancer cell invasion and to
-
topics include (a) AI, machine learning, and large language models for measurement challenges (e.g., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant
-
exhibit hallmarks of active matter. This PhD project aims to develop theoretical and computational active-matter models of early mouse embryogenesis that couple collective cell mechanics with gene
-
neutron scattering (SAXS/SANS) along with theoretical model analysis including the use of multi-scale and artificial intelligence models. The PD will work closely with both the PhD candidates and PIs within
-
projects will have to undergo a check versus national export, sanctions and security regulations. Candidates may be excluded based on these checks. Primary checkpoints are the Export Control regulation
-
and their interactions with peptides. This primarily includes small angle X-ray and neutron scattering (SAXS/SANS) along with theoretical model analysis including the use of multi-scale and artificial
-
probabilistic behavioral models for verification, performance evaluation, and optimization using model-checking techniques, ultimately bridging static system design and dynamic operational analysis. We offer
-
models of early mouse embryogenesis that couple collective cell mechanics with gene regulation. The goal is to identify the physical and mechanochemical principles underlying symmetry breaking and self
-
research modeling future electricity systems in Norway and Europe. The successful candidate will work on the recently funded Research Council of Norway project NORBAT (“Batteries as flexibility providers
-
of Mathematics, and the Department of Philosophy. To respond effectively to the accelerating decline in biodiversity, BioM will unite ecology, statistics, and philosophy to improve the modelling and governance