36 phd-agent-based-modelling Postdoctoral positions at Chalmers University of Technology in Sweden
Sort by
Refine Your Search
-
We are looking for a highly motivated candidate who wants to be at the forefront of research and develop materials and methods for a sustainable future based on renewable materials. This postdoc
-
In this position you will be a leading member of an exciting research group in AI-based software engineering. You will conduct original research, co-supervise doctoral students, teach, and
-
graphene-based field effect transistor sensors with biological receptors for infection biomarkers, and optimize this technology for diagnosing infections in the wound settings. As a postdoctoral researcher
-
for self-deployable 6G networks in the edge continuum (EC-DEPLOY-6G) pioneers the use of large language model–driven agents to autonomously configure and deploy 6G and cloud functions. The project leverages
-
community, focussing on model battery systems, current collector materials, and simplified electrolytes. We will establish a lab-based NAP-XPS at Chalmers in 2026 featuring in-situ/operando battery
-
infection models Microbiology assays, protein purification and biochemical assays MS-proteomics analyses and advanced microscopy Supervise master’s and/or PhD students to a certain extent Possibility to
-
kinases, which will be tested for their capacity to hinder bacterial biofilm formation and virulence. The aim of the project is to generate knowledge-based peptides with the use of generative AI, and test
-
: Experience in matrix hydrodynamics and Zeitlin's model for the simulation of 2-D turbulence. What you will do Supervise master’s and/or PhD students to a certain extent Possibility to engage in teaching at
-
. Research topics include: Development and validation of DORIS data processing and modeling Implementation of improved models for DORIS satellites and ground systems Cross-analysis of DORIS and other geodetic
-
interstellar medium”, funded by the Swedish Research Council (PI: J. Kainulainen). In this project, we combine novel observational data, such as Gaia-based dust density measurements, with advanced modeling