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approaches (e.g. SPG) as well as the use of machine learning, advanced computing, statistical modelling to explore the stochastic response to complex scenarios. This project offers the opportunity to undertake
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. Strong communication skills and familiarity with machine learning, optimisation techniques, geospatial systems, and urban mobility modelling are desirable. This studentship is open to both Home (UK) and
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this overall aim, the student will Employ computer programming methods to determine the occurrence of Alzheimer disease in obstructive sleep apnoea patients using previously collected clinical data and Perform
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that are not seen in any other material. This project combines cutting-edge sampling techniques with machine-learned potentials for accurate phase predictions, offering considerable opportunity for method
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calculations of well-characterized 2D materials, simulations of electron microscopy images, and machine learning methods to reconstruct the 3D atomic positions of materials from a 2D microscopy image. The
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to develop forecasting models. The use of machine learning methods for demand modelling could also be considered. The models that are developed will be implemented in a modelling tool which could be used by
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Applications are invited for a PhD studentship in the Centre for Human-Computer Interaction Design, based in the Department of Computer Science. The successful candidate will undertake PhD research
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
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of (or aptitude to learn) quantitative data analysis and coding (e.g. R). Or a background in computer or data science who can demonstrate their ecological or natural history knowledge. Candidates should have a
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and has a large group of collaborators. You will be joining a great team of supportive and social PhD students working in a high-quality research environment. Learn More: The Dynamics Research Group