17 model-predictive-control Postdoctoral positions at Chalmers University of Technology in Sweden
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high-frequency device behavior Use these models to predict amplifier performance and provide feedback for circuit design and Process Design Kit (PDK) development Collaborate with industrial partners
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We are looking for a postdoc to join our team at the Division of Engineering Materials at Chalmers University of Technology . The research will focus on the use of magnetic fields to control
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Join us to pioneer next-generation generative models that accelerate molecular dynamics. We seek a postdoctoral researcher to develop AI surrogates for molecular dynamics (MD), slashing
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We are seeking a highly motivated and skilled Postdoctoral researcher with interdisciplinary expertise to develop risk assessment and mitigation models using Large Language Models (LLMs
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to predict injuries for the whole population, in collaboration with industry, academia, authorities, and insurance companies. Main responsibilities Computational Modeling and Simulation Develop and validate
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The Division of Vehicle Safety studies accidents, driver reactions, and injury mechanisms. The Injury Prevention group develops Human Body Models to predict injuries for the whole population, in collaboration
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to emerging digital technologies Interplay between technology development and business model evolution - how advancements in technologies reshape value creation and value capture, necessitating continous
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30 Aug 2025 Job Information Organisation/Company Chalmers University of Technology Research Field Engineering » Control engineering Researcher Profile Recognised Researcher (R2) Country Sweden
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within Chalmers and internationally. Chalmers is an employer that actively promotes equal opportunities for women and men. Project overview The project concerns applying supervisory control theory
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large