50 developer-"https:" "https:" "https:" Postdoctoral positions at Chalmers University of Technology in Sweden
Sort by
Refine Your Search
-
Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science
-
This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
-
the society and industry, we strive to solve society's major challenges – together. At the Division of Fluid Dynamics , we develop advanced experimental and computational techniques to investigate flows in both
-
of Geoscience and Remote Sensing , we develop advanced methods and instruments to observe and understand the Earth system. Combining satellite, airborne and ground-based measurements with modelling and machine
-
seek a postdoctoral researcher to develop AI surrogates for molecular dynamics (MD), slashing computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science
-
collaboration meet. The research topics in the department span fundamental and applied research to contribute to the development of a sustainable society. We are Sweden's largest mathematical department, with
-
? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you motivated to develop next-generation electrodes for electrosynthesis
-
to the application deadline What you will do In this poisition, you will be central to the development of the project, and also responsible for the implementation, validation and data analysis of the numerical tools
-
Biochemistry advances multiphase flow and separation science to accelerate industrial innovation and implementation. About the research project The project aims to develop hybrid quantum–classical approaches
-
targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification