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Field
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workflows for grid modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The
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complements our existing scientific software team by bringing dedicated expertise in power plant engineering, process technology, thermal-fluid system design, and techno-economic optimization. Job
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, especially the areas of partial differential equations, integral equations, fast algorithms, spectral and high-order methods * machine learning, especially the areas of optimization, learning theory
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Process Control and Optimization Digital Twin and Modeling & Simulation Predictive Maintenance and Fault Diagnosis Industrial IoT and Edge Computing Good programming skills (e.g., Python, MATLAB, C
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and retrosynthetic analysis software (e.g., AiZynthFinder, ASKCOS, IBM RXN). Familiarity with reaction condition prediction and reaction yield optimization. Exposure to quantum chemistry (DFT) and
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. About us The Department of Computer Science and Engineering , a joint department of Chalmers and the University of Gothenburg, spans the breadth of computing disciplines. Our internationally
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hybrid quantum systems (including open-system and non-Hermitian effects). Design noise-resilient control and optimization strategies, including reinforcement learning (RL) and optimal control approaches
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history, microstructure evolution, residual stress, and final part performance. A strong background in computational mechanics is preferred. 3. Manufacturing Process Development & Optimization: This
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, morphology, and device-relevant properties Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and
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Phd/doctoral Graz, Austria Apply University of Graz Department of Mathematics and Scientific Computing Universitätsplatz 3 8010 Graz Austria The address could not be found. PostDoc Position in the