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funding is available to students undertaking study in: Accounting and Finance Business and Management Science, Technology and Innovation Policy Academic requirements You must meet the minimum academic
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computer literacy, good inter-personal communications skills. Desirable skills: A Master in Health Economics with experience in cost effective analyses. Funding notes The three year studentship covers UK
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Fully Funded PhD Research Studentship tax-free stipend of £20,870 Design, Informatics and Business Fully Funded PhD Research Studentship Project Title: Autonomous AI-powered red agents for enhanced
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. How to apply Applications should be submitted via the Veterinary Medicine and Science PhD programme page, by clicking the 'Apply' button, above. In place of a research proposal, you should upload a
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propulsion. Who we are looking for We are looking for enthusiastic, self-motivated applicants with first-class degree in electrical engineering, Aerospace Engineering or Computer Science with good electrical
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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
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About us Primary Supervisor: Dr Rebecca Baggaley Applications are invited for 1 PhD Studentship for students with a Home Fees status, based at the Institute of Health Informatics, UCL
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computing facilities, supporting applied research in digital engineering. Throughout the project, there will be regular collaboration with BAE Systems, including site visits and access to real industrial use
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identify your programme of study: · search for the ‘Course Title’ using the programme code: 8020F · select ‘PhD Biology (full-time)' as the programme of study You will then need to provide
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co-supervised by both academic and industry experts, gaining valuable skills at the interface of hydrogeology, environmental engineering, and computational modeling. Research Objectives (1