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equations into AI-based models to solve fluid sensing problems in a robust and efficient manner. Your role may include developing new optimization techniques, coding new algorithms, creating new mathematical
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coupled computational framework capable of predicting crack initiation, propagation, and component failure under realistic operating conditions. Key Objectives: - Develop a finite element-based chemo-thermo
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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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, 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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model due to the mathematical challenge of solving the multiple partial differential equations simultaneously. With the support of the combined sponsorship from the university and industrial partner
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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 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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optimization techniques, coding new algorithms, creating new mathematical theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics
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Applications are invited for a fully-funded 42-month PhD studentship with Dr Rachel Nicks and Prof Stephen Coombes on the Leverhulme Trust-funded project White Matter Computation: Utilising Axonal
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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