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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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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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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
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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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cryoEM and cryoET you will ideally have background in at least one of these methods. You also should have a deep interest in mechanisms underlying basic biological processes at the molecular level
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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