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to develop a user-friendly web-based platform that integrates Loughborough's in-house High-Performance Integrated hydrodynamic Modelling System (HiPIMS) with various high-quality open-source datasets covering
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higher education and research, in and outside academia. This PhD position in Political Science offers an excellent opportunity for scientific development through designing, conducting, and disseminating
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) to understand marine food web impacts, ii) changes in ecology at OWFs and other factors (e.g. changes in fisheries, and ongoing and future climate impacts), and iii) cumulative impacts on the natural capital of
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an experienced supervisory team with internationally recognised expertise in stroke rehabilitation science and user-centred intervention design, to develop a programme of study to address these aims. At all stages
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and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data
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for Astrophysics at Potsdam, and the Institute Elie Cartan at the Univ. Lorraine in Nancy, work together on the issue of the relation and interaction between galaxy formation and evolution and cosmic web environment
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structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data
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until 12 noon GMT on Monday 19th May 2025. The Programme Our PhD in Safe AI takes a unique and purposeful approach. PhD researchers with different backgrounds and experiences will train together
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, phycology, and biosciences. Grade average of B or better both for the programme in total and the master’s thesis Drylab skills in molecular biology including DNA extraction, Qubit quantification Experience
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study plan. For a doctoral degree, the equivalent of four years of full-time doctoral education is required. The research group Our lab is advancing precision medicine through deep learning models