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numerical modelling. Ability to participate in research meetings to troubleshoot research problems and discuss the direction of research. Skills Fluency in at least one scientific computer language (e.g., C
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processes, Bayesian inference, signal models, sampling theory, sensing techniques, optimisation theory and algorithms, multi-modal data processing, high-performance computing, mathematical image analysis
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of Engineering, to work on an EPSRC funded project - ‘Engineering Twist'. The project aim is to develop experimental approaches to characterise enriched continuum models for mechanical metamaterials, and their
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We are seeking a talented and motivated researcher to join the Mead Group to contribute to a major research programme focused on characterisation of in vivo models of myeloid neoplasms and
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to join the Mead Group to contribute to a major research programme focused on characterisation of in vivo models of myeloid neoplasms and correlating findings with analysis of patient material. You will
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have proven academic ability and a demonstrable high level of technical competence in computational data science and the analysis / modelling of the results. Theoretical or experimental experience of in
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You will have a PhD in Computer Science or a related discipline or will have obtained it by commencement of the position. Successful candidates will have experience of model training methodologies
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experts to acquire bespoke training and testing data; develop prototype solutions informed by the latest ideas in medical imaging AI, computer vision and robotic guidance; and evaluate models in simulated
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associated computing code for modelling avian influenza outbreaks in Great Britain (GB). One position will focus on modelling the risk of virus invasions into GB in different locations and at different times
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of computational and behavioural neuroscience with modelling and domestic chicks’ data. This position is funded by a Leverhulme Trust project entitled “Generalisation from limited experience: how to solve