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multidisciplinary research team within the School of Electrical, Electronic and Mechanical Engineering. SMG is known for its work in computational mechanics, fatigue, fracture, and advanced materials modeling
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equivalent) in Civil Engineering, Environmental Engineering, Hydrogeology, Geosciences, Environmental Sciences, or related STEM disciplines (e.g., Applied Mathematics, Physics, Computational Sciences
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Mathematics PhD on the Programme Choice page. You will be prompted to enter details of the studentship in the Funding and Research Details sections of the form. Candidate requirements: Applicants must hold
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should have for this project, in addition to our standard entry requirements . Essential Criteria BEng/BSc/MSc in Mechanical Engineering, Applied Mathematics, Applied Mechanics, or related discipline such
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in both research and education with other UCL departments, including computer science, engineering, economics, psychology, public policy, statistics and medical sciences. About the role The UCL School
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, mathematics, engineering, computer science, or related subject) Proficiency in English (both oral and written). Knowledge in cryptography is desirable. Studentship and eligibility The studentship covers: Full
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approach including empirical data analysis, experiments, and theoretical modelling to develop science-based management strategies for the restoration of woodland ecosystems. We will collect, and collate from
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about to obtain, a First or Upper Second-Class UK Honours degree, or the equivalent qualifications gained outside the UK, in Statistics, Mathematics, Data Science or Computer Science. To apply, please
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, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Strong background/skills on machine learning, mathematics, probabilistic
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
relevant field such as engineering, computer science, or applied mathematics. Experience or interest in AI, machine learning, or digital systems is beneficial. We welcome candidates from diverse backgrounds