342 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"UNIS" PhD positions in United Kingdom
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- AALTO UNIVERSITY
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- THE HONG KONG POLYTECHNIC UNIVERSITY
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- VIN UNIVERSITY
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Field
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sustainability. The research will delve into power-aware computing strategies, thermal management, and the development of algorithms that balance performance with energy consumption. Students will aim to create
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-critical systems. The research will focus on developing AI-powered verification tools, health monitoring algorithms, and compliance assurance techniques that ensure system reliability throughout
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that can be validated with experiments and bottom-up models at multiple scales in order to predict the macroscopic response. Hence, this research will investigate the degradation of metallic materials under
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. These problems have been compounded by the emergence of Artificial Intelligence. New forms of algorithmic manipulation have been used to sow discord in democratic societies, undermine trust in politics, and erode
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This doctoral research will focus on the development, optimisation, and coordinated deployment of advanced aerial platforms, specifically electric vertical take-off and landing vehicles (eVTOLs) and
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Award summary This studentship provides an annual living allowance (stipend) of £21,470, and full tuition fees (Home fee level only). Overview This project will develop uncertainty quantification
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Abuse (DVA) experiences of people with Multiple Sclerosis (MS) with a view to highlighting the implications for clinical practice. A recently completed PhD (PhD1) study, supervised by Dr Andrea Hollomotz
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developments such as novel algorithms to support logistics operations, novel automation approaches or the design and development of new digital support tools for logistics providers. Significant flexibility will
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developments such as novel algorithms to support logistics operations, novel automation approaches or the design and development of new digital support tools for logistics providers. Significant flexibility
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy