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both sites. The project sits at the interface of cell line engineering, protein science and machine learning and you will receive advanced training in these areas while developing methods to accelerate
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behaviour across multiple physical models. As the PhD researcher on this project, you will work at the intersection of machine learning, geometry processing and industrial simulation. You will have the
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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
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research, develop and apply state-of-the-art optimisation and machine learning methods to problems within ship design, encompassing hull, powertrain and internal designs. The successful candidate will have
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Energy Storage! Fully funded 4-year PhD Studentship in Chemistry - Machine Learning-Accelerated Quantum Chemical Modelling of Molecular Junctions and Surface Catalytic Reactions PhD Studentship: Taking
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physical models. As the PhD researcher on this project, you will work at the intersection of machine learning, geometry processing and industrial simulation. You will have the opportunity to explore
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The project will prioritise digitising these records using natural language processing (NLP) and machine learning (ML) to create structured datasets. These will support AI applications in paediatric care
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The School of Computer Science at the University of Nottingham is pleased to invite applications for a fully funded PhD studentship in deployable, efficient, and trustworthy computer vision. This is
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with, cloud computing and virtualisation technologies Familiarity and hands-on experience with machine learning techniques desirable Desirable to have work experience (through internships or similar) in
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work with the UK semiconductor industry. The studentship represent a unique opportunity to be trained in the epitaxy process and to work in an emerging and exciting area of combining AI/machine learning