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Statistics and AI for Engineering and Smart Manufacturing School of Mathematical and Physical Sciences PhD Research Project Competition Funded Students Worldwide Dr Wei Xing Application Deadline
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Using Multivariate Statistical Surrogate Models of Displacement Cascades To Simulate High-dose Irradiation Damage Department of Materials Science and Engineering PhD Research Project Directly Funded
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Statistics and AI for Engineering and Smart Manufacturing
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which can also be adapted to students’ interests and experience. Prospective candidates should have an MSc, MMath or MEng in mathematics, statistics, physics, aerospace engineering, signal processing
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, coping mechanisms, and job resources affect these trajectories using discontinuous growth modelling and/or other appropriate statistical analyses. Additionally, the student will conduct a qualitative
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aptitude for research design and statistics and will have a background in psychology or a related discipline. They will be supervised by Dr Sam Farley, Dr Nicola Thomas, and Professor Jeremy Dawson from
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success in the German and/or EU market context. The successful applicant must have strong quantitative, statistical, and analytical skills. Demonstrated knowledge of Python and Machine Learning techniques
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in a relevant discipline (e.g. public health or a social science) Desirable: This PhD project would suit candidates with a strong mathematical or statistical background and an interest in large
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interests. We anticipate the project using primarily qualitative methods, but it may also draw on systematic review methodology, survey methods or statistical analyses of secondary data to identify
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, mathematics, or statistics. - Must have completed, or on the way to completing, a master’s degree at merit or distinction (or a non-UK equivalent) in a relevant subject How to apply: Please complete a