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the commercialisation of all-solid-state batteries. Of particular interest is the development of electro-chemo-mechanical phase field models to predict void evolution and dendrite growth (see, e.g., doi.org/10.1016
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hyperparameter optimization and conformal prediction methods; The online monitoring of model reliability and performance indicators in dynamically changing environments; The integration of synthetic data from
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institutes. This position is based in the Centre for Combinatorics, Algebra & Number Theory. Other Centres include the Centre for Data Science, Statistics and Probability, the Centre for Geometry, Analysis and
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models when faced with data drift, bias, and fairness challenges. The research will involve developing deep learning and synthetic data generation approaches and applying them to exemplar studies in
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Constrained experimental design Combining models and combining data / Realistic simulation of clinical trials Developing LLMs to utilise ODEs and ProbML as tools, Code synthesis for causality Generalisability
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A postdoctoral research position is available in the area of computer modelling of new materials for next-generation photovoltaics within the group of Professor Saiful Islam in the Department
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or time-sensitive semantic modelling Interest in digital humanities or historical linguistics * Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting
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computational modelling, whole-brain fMRI analyses, altered states of consciousness, and information-theoretic approaches. Experience in modelling neural dynamics of neuropsychological disorders, supervising
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research, integrating first-person reports with behavioural and neuroimaging data, developing MATLAB/Python pipelines for fMRI whole-brain models, preparing high-quality manuscripts and visualisations, and
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly