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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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science, technology, engineering, mathematics and medicine (STEM) in higher education. Due to funding restrictions, applicants not eligible for UK home fee status will only be considered in exceptional
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equivalent in a subject relevant to the proposed PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work independently, excellent analytical skills
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. Throughout the PhD, the candidate’s career aspirations will be well-supported. Funding notes: Funding is available through the School of Mathematics for a suitably strong candidate. The scholarship will cover
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Eligibility Criteria You must have, or expect to gain, a minimum 2:1 Honours degree or international equivalent in a subject relevant to the proposed PhD project (inc. computing, mathematics, engineering etc
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Assessment Systems: Toward Trustworthy AI for Complex Educational Evaluation Image and Video Analysis Using Machine Learning Algorithms Mathematical and Computational Neuroscience, from neural data and network
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Science, Physiology, Mathematics or a related discipline. If you have a 2:2 or lower, you may still be eligible to apply if: you have a masters degree in a relevant subject you have three or more years of full-time
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The PhD studentship will be based at the University of Cambridge in the Department of Materials Science and Metallurgy as part of the Structural Materials Group. The Structural Materials Group is a diverse and dynamic research team working across aerospace, automotive, energy, defence, and...
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wellbeing. We are committed to progressing the diversity and inclusion agenda, for example; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and
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, this interdisciplinary project will couple mathematical models of earthworm movement, stochastic models of the measurement process and designed experiments to improve earthworm detection. Project This project will work