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, Physical Sciences, Chemistry, Natural Science, Mathematics. Mode of study Full-time Start date 1 October 2026 Funding This PhD project is in a competition for a Faculty of Science funded studentship. Funding
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profile We welcome applicants with backgrounds in computer science, applied mathematics, or engineering. Essential: strong Python, deep learning experience (PyTorch), and foundations in calculus/linear
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to how well they meet the following criteria: A first-class honours degree (or equivalent) in Engineering, Materials Science, Mathematics or Physics Excellent written and spoken communication skills in
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and optimization of machine learning methods. Candidate’s profile An ideal candidate would typically have: a strong degree or higher qualification in a relevant field (e.g. computer science, mathematics
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Mathematics or Statistics or Computer Science or close to completion, having submitted the thesis at the point of starting the position (Research Assistant)
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2:1 honours degree (or international equivalent) in mathematics, physics, engineering, computer science or other related discipline. To apply, please contact Dr Peter Brearley (peter.brearley
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necessary. Applicants will have a BSc degree/MSc in Computer Science, Mathematics, or a similar computational field. Funding This four-year studentship is funded by Cancer Research UK Cambridge Institute and
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is critical for a wide variety of engineering and environmental applications, remains unexplored. You will join a team of researchers that experimentally quantify the flow-physics of fundamental
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, 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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undergraduate honours degree in Engineering, Mathematics, Computer Science or Physics Excellent English written and spoken communication skills The following skills are desirable but not essential: Background in