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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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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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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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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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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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situ/operando experiments and associated cell design is desirable. Familiarity with one or more of the following techniques is highly desirable: X-ray and neutron diffraction, computational chemistry
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situ/operando experiments and associated cell design is desirable. Familiarity with one or more of the following techniques is highly desirable: X-ray and neutron diffraction, computational chemistry
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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
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the Department of Engineering Science at the University of Oxford. The post is funded by the Oxford Martin Programme on Circular Battery Economies. It is fixed term up to December 2027. You will undertake