23 postdoc-computational-fluid-dynamics PhD positions at Linköping University in Sweden
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) Country Sweden Application Deadline 26 Feb 2025 - 22:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Horizon 2020 Reference Number 853
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application! We are looking for a PhD student in Wireless Communication and Computer Vision. Your work assignments Point clouds (PCs) are sets of three-dimensional (3D) data points and their attributes
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7 Mar 2025 Job Information Organisation/Company Linköping University Research Field Computer science » Other Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Country
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to meter-sized reactors and relatively slow gas flow velocities. Different modeling methods are necessary to cover all aspects of the CVD process. For instance, a combination of computational fluid dynamics
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to cover all aspects of the CVD process. For instance, a combination of computational fluid dynamics (CFD) and density functional theory (DFT) forms a powerful in silico approach to understanding CVD
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and computational methods within quantum mechanics and statistical physics with the aim to design alloys for rare-earth-free high-performance permanent magnets. You will use computational techniques
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part of the national research program WASP. Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research program ever, a major national initiative for
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multiagent dynamics, with special focus on human decisions and opinion dynamics. The research will deal with both theoretical and computational aspects. The student will develop dynamical models and apply them
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novel machine learning method development. However, you will be part of a larger cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities
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of cardiovascular diseases. You will work independently to develop mechanistic models that describe dynamic processes of the microcirculation and analyze large data sets using statistics and deep learning