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to achieve, at least a 2.1 honours degree or a master’s in a relevant science or engineering related discipline. Applicants should have strong background in Machine Learning and Deep Learning. To apply, please
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science, engineering, mathematics, or related subject) Proficiency in English (both oral and written) Essential to have strong foundations in computer systems through degree courses or equivalent work experience
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based on combustion engines, which plays a crucial role for sustainable development and Net Zero. Power electronics converters is a key enabler for vehicle on-board electrical power conversion. Therefore
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backgrounds who are eager to contribute to cutting-edge research at the forefront of nuclear technology. The PhD studentship comes with a competitive stipend. Objectives This PhD opportunity, offered in
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available to students undertaking study in: Accounting and Finance Business and Management Science, Technology and Innovation Policy Academic requirements Candidates must meet the academic requirements
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of Sussex, in partnership with the University of Bristol, has launched the new EPSRC Centre for Doctoral Training in Quantum Information Science and Technologies (QIST) funded via the UK Engineering and
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of the GDPR, Federated Learning (FL) has emerged as a leading privacy-preserving technology in Machine Learning. Despite its advancements, FL systems are not immune to privacy breaches due to the inherent
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master’s (or international equivalent) in a chemical engineering, physical chemistry or related discipline. Experience programming is essential. Knowledge of molecular modelling is highly desirable
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of objects relevant to AWE’s mission. Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related
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alternative models where existing methods prove inadequate. This project is suitable for Engineering or Physics graduates with a strong background in fluid mechanics and heat transfer, preferably with