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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Leverage computer vision, smart
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We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact — today and in the future. TU/e is home...
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. Experience with uncertainty quantification, Bayesian inference, inverse modelling, parameter estimation, or model calibration. Experience with high-performance computing, surrogate modelling, reduced-order
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groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative projects with other group members and our
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theoretically, in tight collaboration with experimental groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative
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an advantage: Rodent social behaviour, empathy-related behaviour, aggression, fear, or reinforcement learning tasks. Computational modelling, Bayesian statistics, reinforcement learning models, or model-based
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. Candidate should have an affinity for multidisciplinary fields of research and a hands-on attitude towards experimental work. Computer skills in simulation and modelling are also desirable. We expect
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. Computer skills in simulation and modelling are also desirable. We expect creativity, flexibility and ability to co-operate within an interdisciplinary research group. Excellent communication skills
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methodologies for battery diagnostics, state-of-health estimation, assessment, disassembly, reconfiguration, and integration. Investigate how traction grids can support high-power charging at construction-sites
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control and grid-forming/grid-following operation; HVDC, MTDC and offshore systems; protection; machine and load modelling; model order reduction; parameter estimation and model validation; large-scale