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Field
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, PhD students, clinicians and computer scientists who will all support the studentship. The successful student will be further supported by members of the wider 4th floor research groups and existing
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with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates, hands on skills in implementation of fusion/learning based techniques in
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., StableDiffusion) and large language models (LLMs) based on the transformer architecture [6] (e.g., ChatGPT). In general, the above generative models need considerable amount of computational resources in terms
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flooding events and recreation in freshwater. This project is part of an exciting new Doctoral Training Programme in Microbial Genomics for Health Protection in collaboration with the UK Health Security
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health genomics, combining academic research with front-line public health intervention experience. This project is part of an exciting new Doctoral Training Programme in Microbial Genomics for Health
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internships An additional disability allowance can be provided where appropriate Students opting to study part-time will receive a pro-rata maintenance stipend. Specified use +3 - covering a full PhD programme
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and to study on this programme. How to apply You must apply through the University’s Application Portal. In ‘Course choice’ tab, put ‘Postgraduate Research’ in 'Type of Study', ‘Full Time’ in ‘Mode
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full postgraduate research programme Please note that existing postgraduate research students cannot be considered for this funding. Tenable period 3.5 years full-time or 7 years part-time. Continuation
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Application deadline: All year round Research theme: Computational Chemistry, Material Science No. of positions: 1 Eligibility: UK students This 3.5-year project is fully funded by industry and home
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PhD programme within QMUL’s Wolfson Institute of Public Health, under the supervision of Dr Giuliano Russo (WIPH) and Prof. Pietro Panzarasa (School of Business and Management). As part of the project