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—or a strong affinity with—Bayesian Statistics, who is eager to contribute to our overarching research programme in high-dimensional statistics. As Associate Professor, you will play a leading role in
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such as Bayesian modelling and optimal control theory. Using state-of-the-art methods – including virtual reality, wearable sensing, motion platforms and advanced data analytics – you will place particular
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expression systems and/or liposomes. You take an interest in liquid handling robots, Python and Bayesian optimisation. You are a team player and enjoy working in a multidisciplinary environment. You have a
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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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software engineering practices. Desirable Skills: knowledge of computer vision, Bayesian theory, or signal processing; experience with prototyping, experimentation, or technical evaluation of interactive
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of neural networks, information-theoretic and optimal-transport perspectives on representation and generalisation, probabilistic numerics and Bayesian deep learning, and emerging frameworks for scientific
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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