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manufacturing sectors. Applicants should have a good background in mechanical, energy, or related engineering disciplines, with knowledge of thermal-fluid sciences, and be familiar with or willing to learn
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-based transfer learning classification model for two-class motor imagery brain-computer interface. International Journal of Neural Systems (IJNS). https://doi.org/10.1142/S0129065719500254 * Kudithipudi
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‑of‑the‑art computational featurisation with experimental reaction‑kinetics data to build a machine‑learning platform capable of predicting catalyst performance. This is an exciting, highly collaborative
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portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications. Equal Opportunities
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points under the points-based immigration system in the UK. For more information please visit: https://www.jobs.ox.ac.uk/pre-employment-checks . Further information on this role can be found in the job
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. Working closely with the Principal Investigator and academic collaborators, you will contribute to cutting-edge research in artificial intelligence, multimodal learning, and machine translation. The role
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may be eligible to be sponsored under the Skilled Worker visa route if tradeable points can be used under the Skilled Worker visa rules. For more information please visit: https://www.gov.uk/skilled
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biology/bioinformatics, statistics, machine learning or related field. You will have a strong track record of applying genetics-based, physicochemistry-based and structure-based computational or statistical
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Libraries and Archives. The role: This new role is an exciting opportunity to build on and enhance the digital learning provision across Cambridge University Libraries and Archives. Through developing high
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to the development of interventions that support people to have healthier lives. The successful applicant will be based in the Manchester Centre of Health Psychology http