461 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof" PhD positions in United Kingdom
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manufacturing processes. This is posing major environmental and ethical challenges. The project will motivate the PhD student to develop next generation electric motors with advanced CNT windings, a promising
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to optimize metagenomic workflows across sample types, developing integrated, sample-specific methodologies. Collaborating with leading academic developers and front line metagenomics users, including
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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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PhD Studentship: Revolutionising Litz Wire Development for Next Generation Ultra-High Speed Propulsion Motors The Manufacturing Technology Centre UK, and University of Nottingham This project offers
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the opportunity for the PhD student to lead the development of innovative simulation tools that predict Litz wire behaviour across electrical, thermal, and mechanical domains. Supported by the MTC’s advanced wire
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. Focusing on adaptive intelligence, which blends human creativity and machine intelligence, the project will develop Multi-Intelligence Agents (MIAs) to facilitate the seamless integration of social factors
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Although the development of systems architectures and models has been an established practice for many years, the assessment of the quality of models has relied on expert judgement with
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High Mountain Asia (HMA), known as Asia’s Water Tower, is undergoing rapid economic development with growing populations in quickly developing countries. However, future development is hampered by
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how standard production practices impact biodegradability is essential for creating more sustainable textile alternatives. This research aims to develop a robust framework for measuring biodegradability
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input needs, accompanied by a boost in algorithmic development, e.g., multi-modal learning, transfer learning, federate learning, and knowledge embedding, etc. However, a significant motivation of