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for light–matter interaction in hyperuniform disordered plasmonic structures, including electromagnetic modelling, optimisation of metal–dielectric–metal resonators, and physics-informed machine-learning
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, and finally using deep learning to solve the complexity challenge associated with coherent beam combination. The role Within HiPPo, your specific task will be to develop a ‘digital fibre laser’, through
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or machine learning. Excellent programming skills in Python and deep learning frameworks A collaborative mindset and interest in socially impactful research. Experience with sign language data, multimodal
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable
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polarisation shaping, and finally using deep learning to solve the complexity challenge associated with coherent beam combination. The role Within HiPPo, your specific task will be to develop a ‘digital fibre
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change and its relevance for present-day sustainability strategies. Alongside your research contribution, you will co-teach modules in Archaeology and Cultural Heritage. About you You have a PhD in
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-reviewed publications and project reports. Spanish language skills are desirable or willingness to learn. You should be willing and able to undertake extended international fieldwork and work across cultural
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component disciplines; in explainable multi-modal deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in
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, learned societies, and industry clients associated with Ingenium Biometric Laboratories. The successful candidate will receive a comprehensive induction at both IBL and UoS and will have access to staff
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications