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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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. Please submit these documents as a single pdf. Please include “PhD Application (Interpretable Machine Learning)” followed by your name in the subject line. The application CV should, at minimum, include
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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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. This project will rely on recent advances in neural networks to develop machine learning potentials (MLPs) for MD simulations of realistic nanomaterial/coolant-liquids and use these to gain fundamental insights
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often fail to preserve the fidelity of combined datasets, leading to loss of crucial information. This proposal aligns directly with the CAMS Data Analytics Theme and the Grand Challenge of using machine
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on the broad topic of Machine Learning and Psychophysiological Deception Detection. The studentship is part sponsored by GCHQ and funded for up to 3.5 years with fees and a stipend at the standard UKRI rate
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Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems How to apply: How many positions: 1 Funding will cover UK tuition fees and tax
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Engineering (PhD) Eligibility: UK Students Award value: Home fees and tax-free stipend £20,780 - See advert for details Project Title: Machine Learning and Optimisation-Based Intelligent Substation Design in
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We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only
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We invite applications for a fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is available to UK (Home) candidates only