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simulations for AI model validation. ✔ Artificial Intelligence (AI) & Deep Reinforcement Learning (DRL) for energy optimization ✔ Predictive Maintenance & Failure Analysis using Machine Learning and Physics
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highly talented and dedicated PhD student with a 1st class or 2:1 degree in the field of Engineering, Mathematics, Physics, Architecture or Computer Science. A MSc degree in a relevant area is desirable
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Research theme: Neuromorphic computing How to apply: uom.link/pgr-apply-2425 No. of positions: 1 This 3.5 year PhD is fully funded for a home or overseas student; tuition fees will be paid and you
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. Successful candidates should have a background in applied mathematics/theoretical physics/computer science or related discipline, alongside strong computational skills. Value: Home fees paid and stipend
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in
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. (2024). Physics Informed Gaussian Process for Bolt Tension Estimation. Proceedings of the 10th European Workshop on Structural Health Monitoring (EWSHM 2024), June 10-13, 2024 in Potsdam, Germany. e
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Following NICE Guidance, NHS England (NHSE) has advocated using Coronary Computed Tomographic Angiography (CCTA) as the first line diagnostic test for suspected symptomatic coronary artery disease
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areas. Cranfield is part of the national testbed for 6G, researching in the following areas of interest: Real-time specification of 6G telecommunication and edge computing services using Large Language
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research. The project will involve computational modelling using physics- and machine learning-based methods and would suit a top student with a background in Physics, Engineering, Mathematics or similar
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enhance system reliability and safety, aligning with the UK’s NetZero targets. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess