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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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perpetuation (or maintenance/persistence); to build ML models that include the heart’s physical properties to find patterns in the data and predict which areas in the heart drive AF. This project will explore
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51 Faculty of Physics Startdate: 01.06.2025 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 31.05.2029 Reference no.: 3938 We invite you to
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of Glasgow, or University of Oxford) to undertake your PhD research. About this Project Project Title: Physical Interactions in Constrained Environments: Reasoning, Sensing, Manipulation and Consensus
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PhD Studentship: Multi-robot Cyber-physical Systems for Solar Farm Health Monitoring and Maintenance
-physical Systems for Solar Farm Health Monitoring and Maintenance Supervisors:Dr Euan McGookin & Dr Ahmad Taha Year 1 MSc Course: MSc Communication and Signal Processing Year 2 – 4 PhD Location: Glasgow
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The use of Artificial Intelligence/Machine Learning methods in process optimisation and control is one of the key strategies the formulation industry has adopted to reduce resource and energy
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, plays a central role in the study of new gauge theories that have the potential to address many of the STFC challenges in Frontier Physics, such as the origin of dark matter in the universe, and the
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candidates from all backgrounds to join our community. At the Department of Applied Physics, our pioneering research in physical sciences creates important industrial applications that hold great technological
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Funding for: UK Students Discipline: Engineering & Technology, Electrical and Electronic Engineering, Other Engineering, Physical & Environmental Sciences Qualification: Doctor of Philosophy in
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representatives to drive meaningful change for the citizens of Wakefield. We would like to support a PhD candidate to undertake research in the following areas: Evaluating and understanding the process and outcomes