60 machine-learning "https:" "https:" "https:" "https:" "https:" "Cardiff University" positions at UNIVERSITY OF SOUTHAMPTON in United Kingdom
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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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aptitude for learning new fields of research. The person should have a PhD, or equivalent qualifications and experience, in aerospace engineering, experimental beam physics, or other ion beam uses. It is
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of intergenerational relations and population change. The Centre is led by Professor Jane Falkingham. For further details, see https://www.cpc.ac.uk/research_programme/connecting_generations/ This project will examine
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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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of the role. Where to apply Website https://www.timeshighereducation.com/unijobs/listing/406449/marie-sklodowska-cu… Requirements Additional Information Work Location(s) Number of offers available1Company
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inclusion and welcome applicants who support our mission of inclusivity. Where to apply Website https://www.timeshighereducation.com/unijobs/listing/406448/marie-sklodowska-cu… Requirements Additional
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to apply Website https://www.timeshighereducation.com/unijobs/listing/405561/research-technician… Requirements Additional Information Work Location(s) Number of offers available1Company/InstituteUNIVERSITY
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the University of Southampton’s wide range of benefits, please visit https://www.southampton.ac.uk/hr/services/benefits-explained/index.page Learn more about Southampton Sport Meet the Team Behind Southampton
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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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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