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applications are made at http://www.bris.ac.uk/pg-howtoapply . Please select PhD in Computer Science on the Programme Choice page. You will be prompted to enter details of the studentship in the Funding and
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should have or expect to achieve, at least a 2:1 (or equivalent) in any engineering degree programme, physics or mathematics. English language requirements: Applicants must meet the minimum
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with: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in
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, to discuss the project or if you have any questions. Entry requirements: Applicants should have, or expect to achieve, at least a 2:1 bachelor’s degree (or equivalent) in biomechanics, sport science
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to produce cutting-edge research. Prospective applicants must: Hold a good honours degree in an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
optoelectronics, light emission, optical sensing and quantum communications. This is an ambitious research program, with a strong interdisciplinary nature, across photonics, plasmonics, device physics, electrical
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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to UKRI minimum stipend Specified use These studentships are open to all nationalities and will be of interest to students who want to undertake research in mathematical sciences. Tenable period 3.5 years
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honours degree in materials science, physics, engineering, or a related discipline. The ideal candidate will be self-motivated, with an interest in both computational modelling and practical manufacturing
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. The start date is October 2025. Are you passionate about applying computational science to real-world engineering problems? Do you want to develop digital twins of materials that can predict performance and