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tools are need during the development of new imaging and sensing systems. With the rapid deployment of data-driven methods, repliable uncertainty quantification remains a big challenge that requires
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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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temporal properties: ultrabroadband supercontinua, intense sub-cycle field transients, and few-femtosecond ultraviolet pulses, among many others. We combine numerical modelling with experiments to study the
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quantum field theories, and the application of Hamiltonian methods to gauge theories, though you will also be encouraged to develop and pursue your own research directions. Applicants should have a PhD in
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the context of teaching and supervision duties. Could be expected to contribute to specialist courses such as research methods and equipment. Develop research objectives and proposals for own or joint research
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: Ultra-fast-laser-welding of optical laser components to structural components (ps- and fs-pulsed lasers) Test methods of evaluating optical and mechanical parts subjected to thermal and environmental test
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. Hardworking, punctual, proactive, willing to learn new methods and techniques. Must be able to work well independently but also as part of a team, providing leadership to junior colleagues. Flexible, self
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candidates will have expertise in computational imaging, with: (i) an algorithmic focus, with particular interest in methods at the interface of deep learning and optimisation theory, and/or (ii
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-laser-welding of optical laser components to structural components (ps- and fs-pulsed lasers) Advanced utilisation of powder-bed laser fusion metal additive manufacturing. Test methods of evaluating