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Northeastern University London Fully Funded PhD Scholarship in Computer Science Reliable Quantum Statistics Northeastern University London > Computing, Mathematics, Engineering & Natural Sciences
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slowly, or act quickly but lack robust planning. Frontier models typically depend on heavy compute, cloud inference or controlled settings, limiting real-world use under compute, latency and energy
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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
determination by developing mathematical methods and integrating machine learning into crystallographic workflows. The successful candidate will develop theoretical and computational approaches to improve
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This post (Research Assistant/Associate in Computational Methods for Structural Wing Design) will allow you to contribute to a major industry-funded project developing the next generation of
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are excited to work across disciplines and contribute to cutting-edge research at the interface of biology, chemistry, imaging, computation, and AI. Molecular Perturbations: Chemistry Engineering Biology
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-year Computational Project to use state-of-the-art Computational Chemistry techniques to understand structure-property relationships in thermal batteries, and to derive new understanding to guide design
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Despite significant advances in numerical techniques and computing hardware, the high computational cost of large-scale 3D computational fluid dynamics (CFD) modelling remains a major challenge. A
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duration: 3.5 years Sponsor: School of Computing Supervisors: Dr Mujeeb Ahmed Eligibility criteria You must have, or expect to gain, a minimum 2:1 Honours degree or international equivalent in a relevant
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only of storing information, but also of manipulating, organising, and retrieving it through molecular data structures analogous to those used in computer science. Building on our pioneering experimental
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep