22 phd-numerical-analysis Postdoctoral positions at Chalmers University of Technology
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to contribute your own research ideas and take part in supervising PhD students. About the research project The position, starting in the first half of 2026, will be based in the theory division of the Department
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with international partners in the FLAG-ERA project ThinQ. You will also have the opportunity to contribute your own research ideas and take part in supervising PhD students. About the research project
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finite element models using LS-DYNA, OpenRadioss, or equivalent solvers to simulate skull fracture mechanics during impact scenarios Perform parametric studies for population-level analysis Develop
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, or equivalent solvers to simulate skull fracture mechanics during impact scenarios Perform parametric studies for population-level analysis Develop protocols for various fall scenarios and demographic parameters
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fluid dynamics and vascular modeling in microenvironments Skills in data analysis and image processing (e.g., Python, R, ImageJ) Ability to mentor junior researchers and contribute to team leadership What
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different mixing and reactive properties compared to conventional fuels. In this project, turbulent mixing and combustion of hydrogen in air will be studied through optical experiments and numerical modelling
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). This position offers a unique opportunity to collaborate closely with researchers across the Division of Marine Technology at Chalmers University, with a focus on maritime transportation risk analysis. Project
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PhD students in the Automation group, with the primary goal of qualifying for a future academic career. Contribute to research projects within discrete-event systems, supervisory control theory, and
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the project’s Principal Investigator, and the practical implementation of this research with the Secura Lab. The role also offers ample opportunities to mentor PhD students, supervise MSc projects, and engage
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fluids, flow-induced pattern formation in both simple and complex flows (e.g. flow instabilities, product defects), multiscale analysis, and the application of machine learning techniques. About the