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knowledge of methodologies such as deep and statistical learning. Informal enquiries may be addressed to Prof. Andrea Vedaldi (email:andrea.vedaldi@eng.ox.ac.uk) For more information about working at
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on the training strategies. In this project, we will investigate Bayesian methods to train deterministic SNNs (with deterministic activation functions) or probabilistic SNNs. Bayesian deep learning methods have
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implementation of deep learning and computer vision frameworks across a range of research projects. This includes developing and training deep learning models for tasks such as scene understanding, object
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50 Faculty of Life Sciences Startdate: 01.08.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 31.07.2029 Reference no.: 4160 Explore and teach
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analysis of PDEs (with deterministic and/or stochastic methods), Gaussian Random Fields, mathematical foundations of deep learning, functional analysis and measure theory. You can find more information about
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areas of concerns to improve healthcare delivery to people with a learning disability and autistic people. We are contracted to deliver an annual report, regional reports and a number of deep dives as
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deep exploration of cancer precursors (precancers) to identify their molecular vulnerabilities and developing methods to intercept them. The alliance is led by Professor Sarah Blagden. You will be
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the aim of conducting deep exploration of cancer precursors (precancers) to identify their molecular vulnerabilities and developing methods to intercept them. The alliance is directed by Professor Sarah
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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should be an expert in at least one of those techniques and keen to learn the others. You also should have a deep interest in molecular mechanisms underlying biological processes and – if not experience