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(or biomedical PhD with strong computational skills). Fluency in modern AI/ML and emerging paradigms (agents, explainability). Track record of computational solutions to biological problems. Clear communicator
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polycrystalline material during plastic deformation in order to eventually predict the manner in which materials deform and fail. As a first step, we wish to infer a distribution of the directions of deformation
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to engage with multidisciplinary teams and external partners. Desirable attributes include experience with spatio-temporal models, machine learning, Bayesian methods, and knowledge of environmental exposure
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networks, Bayesian inference, computational neuroscience, mathematics.
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networks and/or probabilistic graphical models; and causal inference. An outstanding publication record in top tier machine learning and/or computer vision conferences or journals, commensurate with
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$7,000 per year top-up is also available, contingent on candidate performance. Scholarship Details Maximum number awarded 1 Eligible courses Any applicable PhD courses Eligibility criteria A strong track
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learning and one or more of the following: transformer networks, implicit neural functions, graph neural networks and/or probabilistic graphical models; and causal inference. • An outstanding publication
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required, integrate multiple data sources to seek trends and analyse issues from diverse perspectives and draw sound inferences and specific challenges from information available. Collaborate closely with
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understanding of non-stationary complex systems through theoretical analysis and numerical simulation develop efficient statistical algorithms for analyzing and inferring dynamical models from multivariate time
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data, make sound inferences, interpret and apply complex resolutions and policy, and communicate results in written reports effective and tactful communication skills (both verbal and written), strong