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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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join an interdisciplinary team and have the opportunity to develop new methodologies for analysing geological and commercial opportunities for deep geothermal energy in the UK. The NNZA-funded project
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on understanding the spread and control of human infectious diseases using modelling and pathogen genomics. This is a short-term opportunity to apply machine learning methods to two key projects. First, you will
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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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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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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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precisely monitor changes in the global distribution of CO₂ sources and sinks. The UK science lead for MicroCarb is at the University of Edinburgh so this opportunity is a valuable opportunity to gain a deep
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for deep access within the human body. The research remit will encompass: mechanical and electronic circuit design, prototyping, and pre-clinical evaluation of diagnostic and therapeutic devices to tackle
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rules which enable effective learning in large and deep networks and is consistent with biological data on learning in the cortex. In particular, the research will focus on evaluating and extending a
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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