19 algorithm-development-"Multiple"-"Prof" "NTNU Norwegian University of Science and Technology" Fellowship positions at UNIVERSITY OF SOUTHAMPTON
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Innovation (UKRI), focusing on populations with multiple long-term conditions. You will contribute to a social care initiative, developing and testing an AI-informed digital tool to help individuals with
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Innovation (UKRI), focusing on populations with multiple long-term conditions. You will contribute to a social care initiative, developing and testing an AI-informed digital tool to help individuals with
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the laboratory of Professors Ward and Ober. Their interdisciplinary research program is dedicated to the development of novel antibody-based therapeutics that has led to several therapeutics that are currently in
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. This role will contribute to the project by: Refining the flow battery design for manufacture and installation in Nepal Developing the control system/BMS for integration with a wider energy management system
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based at the School of Electronics and Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing
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Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing techniques to enable large-scale data search
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-term dielectric performance, and your work will assess the evolution of a number of key dielectric properties over 6 months of accelerated ageing tests in the TDHVL. Join a multi-disciplinary team at
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will be based in the Environmental Lab at the University of Southampton, with opportunities to collaborate with leading researchers across multiple institutions within EBIC. The research will be
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complex health and social care challenges, particularly in the management of long-term conditions. We combine AI and traditional epidemiology with qualitative methods to develop impactful, real-world
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Science in the University of Southampton. ActivATOR will develop novel machine learning models that enable robots to leverage the motion of their own bodies (‘egomotion’) to make sense of acoustic environments