400 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD positions in United Kingdom
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About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat
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devices for medical imaging and reaction monitoring, as well as for the development of sustainable photocatalysts. In this role you will develop machine learning (ML)-accelerated quantum mechanics in
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results is desirable. To be considered for this PhD, please follow the instructions here: https://www.centre-ub.org/studentships/application-process/ Application deadline: February 17 2026 Interviews
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an adaptable Machine Learning (ML) hardware architecture to solve Artificial Intelligence (AI) classification tasks using Internet of Things (IoT) sensor data. This will be a small system-on-chip designed
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The always-on, safety-critical nature of air traffic control raises rich and exciting challenges for machine learning and AI. The University of Exeter in partnership with NATS, the UK’s main air
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reservoirs. By embedding governing equations and boundary conditions directly into machine-learning models, the project aims to enable efficient exploration of high-dimensional parameter spaces without
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the supervisor’s complimentary research expertise in this area (https://millerresearchgroup.co.uk & https://www.lovelockresearchgroup.co.uk),[8,9 ] this PhD will involve the design and chemoenzymatic synthesis of a
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About the project: Machine Learning to Unlock the Mysteries of Metallic Phase Transitions Supervisor: Dr Livia Pártay, University of Warwick Join a PhD project that goes beyond state-of-the-art to
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their pandemic potential and classification as bioweapons. This project aims to develop a machine learning-accelerated NMR platform for the discovery of high-affinity inhibitors targeting viral RNAPs. Building
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling