629 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" scholarships in United Kingdom
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measurement and stopping rules controlling type-I error with finite-sample guarantees. Quantum statistical modeling: use of quantum relative entropy, Fisher information, and variational quantum circuits
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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for more information. Eligibility Applicants must meet the following eligibility criteria to be considered for the scholarship: Priority for the scholarships will be given to students from low socioeconomic
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qualification). Further information Informal enquiries to Dr Beth H. Richardson (bhrichardson@lancashire.ac.uk ) For full details: Apply here: online application system . Quote reference NWSSDTP CASE Studentship
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reusable plaque–flow atlas. Key objectives include to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric
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formed during late-stage deglaciation and subsequent marine transgression. These data will provide critical constraints for palaeoclimatic reconstructions and help quantify the magnitude and style
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geophysical and planetary models. IBM faces several such challenges in practice, many of which are currently approached using Foundation-Model (FM) surrogates. However, certain inverse problems arise in data
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that deliver healthier indoor environments, lower carbon emissions, and long-term building performance. By integrating Passive House and EnerPHit principles with real building data, the research will support the
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the Faculty of Engineering, plus those housed in Plant Biosciences at the Sutton Bonnington campus. Data sets will be generated using simulated and experimental data and these will be used to train networks
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for candidates who can demonstrate strong research potential. Suitable backgrounds include, but are not limited to: Human Factors Law (particularly law and technology, medical law, or data governance) Psychology