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Field
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interests include flow of soft materials, non-equilibrium dynamics, dynamics of soft glasses, statistical physics of yielding, shear thickening of dense suspensions, phase behaviour, self-assembly, fluid
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theoretical understanding of statistical machine learning methods relevant to the project: Bayesian learning, machine learning, spiking neural networks. Experience of programming (e.g. with Python) and data
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sustainability, performance, and reliability. Our research leverages optimization techniques, applied machine learning, and statistical analysis to achieve these objectives. Through the DecAI project we will work
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conferences. It is essential that you hold a PhD/DPhil in computational biology, genomics, bioinformatics, computer science, statistics, or a related field together with strong programming skills in Python, R
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students. It has an active programme of internationally recognized research in Pure Mathematics, Applied Mathematics, Statistics and Probability. The research culture is vibrant, with many visitors, seminars
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of internationally recognized research in Pure Mathematics, Applied Mathematics, Statistics and Probability. The research culture is vibrant, with many visitors, seminars, international conferences