42 algorithm-development "https:" "Simons Foundation" PhD positions at Newcastle University in United Kingdom
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Joint Faculty of Medical Sciences (FMS) and NIHR Applied Research Collaborative North East and North Cumbria (ARC NENC) PhD Studentship – Developing a risk-based care pathway for patients with
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Interested in how to shape the future of care for patients suspected of having a TIA? This PhD will explore how a risk-stratified approach could be developed with key stakeholders. National guidelines advise
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PhD Studentship: Development of a robust hydrological modelling framework for drought risk assessments Award Summary Tax-free annual living allowance £26,546 a £20,000 research training grant and
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systems with "self-diagnosis" and "self-healing" capabilities. By integrating federated learning, graph neural networks, and blockchain technology, we will develop a framework that moves beyond static
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) algorithms in realistic network settings and explore hybrid approaches that combine classical and post-quantum techniques. The project will involve protocol design, system-level evaluation, and performance
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Integration: This WP develops a Runtime Assurance Layer by deploying lightweight anomaly detection algorithms, such as autoencoders, to flag unsafe AI decisions. It also involves the development of an Ethical
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have shown that the micro-scale maps they generate can be analysed using computational geography methods developed for macro-scale data. Our toolkit, STExplorer, enables access to these approaches
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essential for cartilage development, skeletal growth, and adult joint maintenance. Enhancers—non coding DNA elements that fine tune gene expression—play a central role in controlling SOX9 expression. We have
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prestigious £8M EPSRC Programme Grant on Advanced Integrated Motor Drives (AIMD), a major research initiative launched in 2025. You will work on the development of wide bandgap device-based integrated motor
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of £25,726 plus a research training support grant of £20,000 and 100% fees paid. Overview This PhD project aims to develop a computationally efficient framework for the real-time prediction of river water