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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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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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successful candidate will possess skills in natural language processing and deep learning. Experience of studying the robustness and generalisability of LLM would be beneficial. This is a full time post (35
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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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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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are comfortable navigating complex HPC environments and wrangling large datasets. You have experience with modelling through state-of-the-art machine and deep-learning methods and with hands
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Control engineering (experience with nonlinear systems is a plus) Machine learning and deep learning in context of physical systems Programming skills are required, with Python experience preferred. A good
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sufficient theoretical knowledge of deep learning-based methodologies as well as working with real-world data. Informal enquiries may be addressed to Prof Alison Noble (email: alison.noble@eng.ox.ac.uk
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assemblages and morphometrics, sedaDNA and the deep microbiological biosphere), as well as applying other dating techniques including radiocarbon, OSL and palaeomagnetics. In addition to having the opportunity