376 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"IRIBHM-ULB" PhD positions in United Kingdom
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motivated candidate with a 1st or high 2:1 degree in mechanical engineering, materials science, or a related field. Interest in advanced manufacturing and sustainability is essential. Experimental and data
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Professor Noel Healy at noel.healy@ncl.ac.uk For further information about the School of Mathematics, Statistics and Physics please click here . As part of our commitment to career development for research
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, please complete the steps below. PDF versions are available here to help with planning but submissions must be made using the forms above, emailed PDFs will not be accepted. Information Session There will
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migrant and non-national groups. PhD Focus and Research Questions The PhD will explore how AI and digital data systems are changing everyday welfare practices in the UK. The student will likely focus on one
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the wider custodial role of cultural institutions remain under-recognised. Working closely with the Serpentine’s R&D Strategic Lead, the project will develop new approaches to research, data gathering
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knowledge for sustainable formulation design. Using different data-driven feature representations, AI foundation models will generate chemical embeddings to predict key physicochemical properties. Coupled
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evidence of programming experience. How to Apply Applications should be submitted through the RAINZ CDT website via the above 'Apply' button by 13 February 2026, where further information about the CDT is
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to the interests of one of the School’s research groups: Cyber-physical Health and Assistive Robotics Technologies Computational Optimisation and Learning Lab Computer Vision Lab Cyber Security Functional
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information and a detailed description of the research project, hit the 'APPLY' button above. Application Deadline: This PhD studentship offer will remain open until filled.
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with Materion’s Technology & Innovation roadmap. Perform materials and component testing using Materion and Imperial facilities. Analyse data, evaluate results, and implement solutions to manufacturing