355 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" PhD positions in United Kingdom
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within physically sensible design spaces avoiding the need to learn every pathological flow scenario and making machine learning both efficient and reliable. The ultimate goal is to retain the fidelity and
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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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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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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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from working closely with its team of post-docs, associated researchers and partners (that range from Microsoft Research to the NHS). For this project you should have a strong interest in AI/Machine
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engineering, machine learning, molecular design, and sustainability, helping to create smarter ways of identifying promising sorbents for electrochemical CO2 capture. Over the course of the project
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bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 30.04.2029 Reference no.: 5311 Your responsibilities: As a University assistant, you will contribute to the work group Machine Learning
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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