17 algorithm-development-"Multiple"-"Prof" "UNIS" Fellowship positions at UNIVERSITY OF SOUTHAMPTON in United Kingdom
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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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child health. The position is based at the MRC Lifecourse Epidemiology Centre, and part of an NIHR-funded programme of research which aims to inform how people can be better supported to plan and prepare
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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