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Application deadline: 15/08/2025 Research theme: Computer Science No. of positions: 1 Eligible for: UK This 4-year PhD project will be funded by DLA studentship and is open to UK students
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Computational verification of high-speed multi-material flows, where physical experimentation is highly limited, is seen as critical by the defence Sector (source: the UK Atomic Weapons Establishment). Recent work by the group (leading to REF 4* rated outputs and several Keynotes) has...
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, Pennsylvania 19004, United States of America [map ] Subject Areas: Industrial Engineering Statistics Mathematics Management Science & Engineering Computer Science and Electrical Engineering (more...) Operations
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PhD Stipends within Distributed, Embedded and Intelligent Systems (DEIS) At the Technical Faculty of IT and Design, Department of Computer Science, one PhD stipend is available within
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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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Funding: School of Computer Science studentship consisting of the award of fees, together with a tax-free maintenance grant of £20,780 per year for 3.5 years. Lead Supervisor’s full name & email
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. We are looking for highly motivated candidates with a strong academic background in computer science, AI/ML, bioinformatics, or related fields such as mathematics and statistics. Informal enquiries
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Artificial Intelligence (applied mathematics, computer science, etc.), or a thesis defense scheduled for 2025. • Research contributions in deep learning, statistical learning, natural language processing (NLP
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Areas of study Neuroscience, Physics, Applied Computer Science, Bioinformatics, Computer Science, Engineering Informatics, Medical Informatics, Chemistry, Analytical Chemistry, Inorganic Chemistry
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computer science and information system engineering while collaborating in a publicly funded research project with our industry partner Encevo. In this project, the aim is to analyze and build a robust anonymization