35 machine-learning "https:" "https:" "https:" "https:" "https:" "VIETNAMESE GERMAN UNIVERSITY" Fellowship research jobs at UNIVERSITY OF SOUTHAMPTON in United Kingdom
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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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the project forward and translate our research into tangible innovations for the UK’s growing photonics sector, building upon our recent work published in Nature (https://www.nature.com/articles/s41586-025
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(W.L.King@soton.ac.uk ). Working at the University of Southampton: Check out the staff benefits and why you should join us at The University of Southampton. Where to apply Website https
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to a flexible working approach. Where to apply Website https://www.timeshighereducation.com/unijobs/listing/407130/research-fellow-in-… Requirements Additional Information Work Location(s) Number
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shaping contemporary political landscapes. Project Overview: Website: More information is available at the PLEDGE website: https://www.pledgeproject.eu/ ; Responsibilities: Engage in conceptual and
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to help maintain and support employees’ well-being and work-life balance, please see our working with us webpages Where to apply Website https://www.timeshighereducation.com/unijobs/listing/406574/clinical
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on Smart Fibre-Optic High-Power Photonics (HiPPo). The HiPPo programme (https://www.hippo-laser.co.uk/ ) is focused on understanding how to control the properties of fibre lasers, to go beyond the “fixed
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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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Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates would have a background in machine learning, manufacturing
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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