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services would be to keep children close to their usual home (where safe to do so) and maintain their family and community networks often this is not possible and high cost out of area placements
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candidates are invited to apply promptly as selections will be made on a rolling basis. Ideal candidates would have a strong background in Computer Sciences, Software Engineering, Artificial Intelligence
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economic studies funded by the UK National Institute for Health and Care Research. Experience of conducting economic evaluations using suitable statistical software (e.g. STATA, R or SAS) is essential
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persists, even for the most powerful sensors operating in this way. A drastic departure from this sensing architecture is “multistatic” radar – enacted by a coherent network of spatially distributed sensors
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isolation, leading to suboptimal network performance. This project seeks to address this gap by developing a holistic, system-level approach that optimises PST deployment strategies to enhance grid
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be carried out under the UK Hypersonics Doctoral Network, which has been supported by the Ministry of Defence and EPSRC for building the necessary expertise to develop next-generation hypersonic
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to protect our planet from asteroid hazards. The aim of the project is to deliver and validate of an onboard astrodynamics GNC software for autonomous proximity operations around minor celestial bodies
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of Science and Technology (proud member of the Alan Turing University Network) and be supervised by leading experts in machine learning for healthcare. You will also be affiliated to the School of Health
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generative modelling, and graph neural networks. Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with teaching; liaising and
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short courses in the core subjects of this PhD programme including process intensification and green chemistry. This project is part of the Process Industries: Net Zero (PINZ) Centre for Doctoral training