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to learn from. Augment Data with Generative AI - You will use physics-constrained generative models (Diffusion models/GANs) to create a diverse library of synthetic microstructures, improving model
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role in supporting data-driven, policy-relevant research examining energy markets and a range of associated social and environmental challenges. This role is ideal for candidates planning to apply to top
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and experimental data, the student will develop predictive models of strand displacement reactions in non-ideal settings, in collaboration with the other students within BIOHYBRITE. These areas include
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stakeholders and an analysis of TIA clinic data you will help to understand how stratification might assist in delivery of more appropriately targeted care. You will develop mixed-methods expertise and join Dr
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. This PhD project benefits from a specialist postgraduate environment with strong links to business, industry, and policy. Cranfield School of Management’s Centre for Logistics, Procurement and Supply Chain
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Project Link: Image guided intervention supported with mixed reality | Project Opportunities | PhD | University of Leeds Eligibility: UK and International Funding: School of Computer
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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will
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across disciplines, linking ecology with data-driven analysis; How to apply Interested applicants should contact Dr Weizhuo Wang (w.wang@mmu.ac.uk ) for an informal discussion. To apply you will need
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project will transform rain garden design by linking plant traits to hydrological and ecological outcomes, ensuring resilience under alternating drought and flood stress. Working with the Royal
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approaches that overlook plant ecology and the interaction with soil structure development. This PhD project will transform rain garden design by linking plant traits to hydrological and ecological outcomes