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Field
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Research and development of data-driven and probabilistic planning methods for informative and adaptive environmental sampling. Development of sensor-fusion-based estimation and environmental field
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an interdisciplinary undertaking, involving biochemistry/structural biology, molecular and organismal genetics, biophysics, biostatistics, bioinformatics, and theoretical physics. Recently, AI (AlphaFold, computer
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hierarchical Bayesian models to cognitive processes Supervisor: Dr Martin Lages Project aims: New variants of hierarchical Bayesian models of cognitive processes can be investigated using simulated, existing and
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-objective, real-time) and supply-chain optimization; PdM and RUL with health monitoring; digital twins/smart factories, cross-site transfer and federated/edge learning; uncertainty estimation and calibration
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on estimation of parameters relevant to dynamics and control using innovative statistical methodologies. The advent of new sequencing technologies will revolutionise the way we study epidemiology, particularly
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. Essential Functions/Responsibilities Expertise in all areas of machine learning including deep learning, Bayesian statistics, probabilistic modeling, optimization, learning theory, high dimensional data
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., Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models. PLoS Comput Biol 15, e1007492 (2019). Overview Overview The MRC-University of Glasgow Centre for Virus
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the Faculty of Science. We will apply Bayesian approaches such as the information-theoretic minimum message length (MML) principle and other approaches to develop a path towards statistically-optimal algorithms
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for the prediction task, such as predicting the bioassay of a given chemical network. One of the approaches that will be considered will be the Bayesian information-theoretic Minimum Message Length (MML) principle