10 atmoshpheric-modeling Postdoctoral positions at Chalmers University of Technology in Sweden
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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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This multidisciplinary position is part of a WASP NEST (Novelty, Excellence, Synergy, Teams) project focused on advancing generative models and perceptual understanding in computer vision. The
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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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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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thermal conductivity b) Development of hBN thermal interface material that combines a high degree of compressibility and recovery c) Modeling, simulation and characterization of phonon transfer across
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Parkinson. We use in vitro biophysical analysis to characterise protein aggregates and their formation in combination with advanced live cell fluorescence imaging and cell model development to study protein
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tomorrow. About the department The main competences at the Department of Industrial and Materials Science are found in the areas of: Human-Technology Interaction Form and Function Modeling and Simulation
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qualitative data into quantitative models. A track record of high-quality publications. The position requires sound verbal and written communication skills in English. Swedish is not a requirement but Chalmers
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large