449 web-developer-"https:"-"https:"-"https:"-"EURAXESS" PhD positions in United Kingdom
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Turbulence detection in blood flow using 4D MRI Flow disturbances in blood flow are vital sign of cardiovascular diseases, suggesting a development of turbulent flow due to abnormal heart movement
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early detection and predict adverse pregnancy outcomes. You will develop and validate a data-driven clinical decision support tool in collaboration with clinicians and industry partners. Pre-eclampsia is
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collaboration with BP. Vision We are seeking PhD student that is motivated to develop practical rapid analysis tools for BPs biomass liquefaction processes. We are looking for candidates with an interest in
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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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environment. Understanding the factors related to effective silencing is key for the development of novel controls. In collaboration with CRODA, JIC have developed RNA based product testing systems. Using our
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. Chem. Soc. 2021, 143, 9813), and developing their reactivity (e.g. Nat. Commun. 2020, 11, 337). Nevertheless, there are still many elusive actinide-ligand multiple bonds that would be ground-breaking
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this, empirical rules have been developed. However, a fundamental understanding of the process is still lacking. Furthermore, current standard tests do not adequately capture the phenomena, and thus industry is
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develop advanced skills in qualitative research, stakeholder engagement, and knowledge exchange, alongside being supported to publish in leading journals and present at conferences. You will also benefit
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at the intersection of theory, computation, and high‑fidelity simulation, the successful candidate will contribute to the development of a novel ensemble-based framework for analysing driven perturbations in wall
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this project, you will develop the next generation of federated machine unlearning algorithms—methods that can efficiently deliver genuine, verifiable, and robust erasure without sacrificing model performance