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We are seeking a full-time Postdoctoral Research Assistant to join a cross disciplinary research project to improve our understanding of colorectal cancer. Deep learning has revolutionised image
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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with deep learning libraries (e.g., PyTorch) Ability to organise and prioritise work to meet deadlines with minimal supervision Strong written and verbal communication skills, with the ability to convey
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/C++; hands-on experience with deep learning libraries (e.g., PyTorch) 5. Ability to organise and prioritise work to meet deadlines with minimal supervision 6. Strong written and verbal
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require a deep understanding of the classical infrastructure that supports them, including analog control systems. As quantum devices scale toward the million-qubit regime, modeling these complex systems
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on qualifications and relevant skills acquired and will also be determined by the funding available. About you Applicants will hold a PhD/DPhil or be near completion of a PhD/DPhil in a subject relative to Structural
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journal publications dependent on your background discipline(s) and should hold sufficient theoretical knowledge of deep learning-based methodologies as well as working with real-world data. Informal
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and Mind Building, South Parks Road, Oxford Applicants must hold a PhD in Microbiology and/or Molecular biology and will be responsible for providing microbiological data to facilitate the design of new
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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