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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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. 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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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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Are you passionate about keeping communications networks secure and trustworthy? Are you eager to explore attacks and countermeasures for QKD systems and to help ensure they can deliver the promised
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unprecedented pressure on urban electricity networks resulting in Grid congestion. In particular, construction sites often require high-power charging in locations where grid connections are unavailable
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, networks and sensors that can be deployed in the world. The functionality of these technologies is already articulated to some extent, but applications relevant for people, industry and governments are less
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shaving. Job description The rapid electrification of transport, buildings, and industry is creating unprecedented pressure on urban electricity networks resulting in Grid congestion. In particular
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or synthetic network modelling. Experience in converting or interfacing models between different simulation tools. Experience in contributing to courses and training. TU Delft (Delft University of Technology
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-reviewed academic journals and practitioner magazines; Present your research at national and international academic and practitioner conferences and actively develop your (academic and practitioner) network