41 phd-in-mathematical-modelling-of-biochemical-reactions PhD positions at Chalmers University of Technology in Sweden
Sort by
Refine Your Search
-
the right one for you! This is a fully funded PhD position to develop micromechanical models of high-pressure die-cast aluminium, a unique opportunity for a motivated individual to work in a collaborative
-
We are looking for a PhD candidate fascinated in modelling erosion processes in sensitive clay slopes. The highly sensitive clays, called quick clays, can change from solid to liquid with small
-
cells. Reference number: 20250273 Application deadline: August 10, 2025 Project overview This 5-year PhD project aims to develop a flexible and general model that enables comparison between different
-
We are looking for a PhD candidate fascinated by the response of sensitive clays, such as quick clays that can change from solid to liquid with small environmental perturbations. We want to be able
-
format. This will allow combinations of neural networks with physics models. The project brings together PhD students and senior researchers from multiple disciplines to tackle challenges in sustainable
-
materials for synthesizing different types of hydrogen storage molecules. Using advanced quantum mechanical calculations, you will develop multi-scale models to study reaction kinetics and improve catalyst
-
Analysing biofilm structure and microbial communities Additionally, you will develop a mathematical model that includes both adsorption and biodegradation mechanisms. This model will be calibrated using pilot
-
We are looking for a PhD candidate fascinated by the response of quick clays that can change from solid to liquid with small environmental perturbations. We want to be able to understand how a
-
with expertise in materials characterisation, computer vision, computational modelling, and machine learning. The other PhD positions connected to the project are: PhD Student Position in Generative
-
This PhD position is part of the WASP-WISE NEST project RAM³ – a multidisciplinary research effort at the intersection of machine learning and materials science. The project brings together PhD