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motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
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Details Panel (longitudinal) data enables learning the dynamics and relations of (groups of) units, strengthening the inference on both cross-sectional and dynamic parameters. The dominant approach
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physics, Bayesian inference, and complex systems theory. You will contribute to method development, simulation and validation in close collaboration with experimental partners. Key Responsibilities: Carry
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Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos, Dr Dimitrios Roxanas Application
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. Compare advanced deep learning–based methods with probabilistic approaches. Collaborate with researchers at Chalmers, the University of Gothenburg, and international experts in Bayesian inference and
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. Collaborate with researchers at Chalmers, the University of Gothenburg, and international experts in Bayesian inference and optimal control. Present your results at international conferences and publish in
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silico model of normal development. Bayesian inference will calibrate model parameters and highlight control points, with predictive accuracy benchmarked against existing perturbation datasets. O3. Map
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and learned surrogates with clear statistical validation; Bayesian inverse problems and data assimilation via measure transport and amortized inference; robustness and distribution shift in scientific
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Bayesian ML approaches for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and
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Scalable Inference: Develop new algorithms for scalable uncertainty quantification (UQ) and Bayesian inference and apply them to challenging simulation problems. The goal is to produce robust, validated