567 data-"https:" "https:" "https:" "https:" "https:" "https:" "SciLifeLab" "IFM" "IFM" "IFM" PhD positions in United Kingdom
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progression Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes). Further Information We are hoping that the successful candidate will start their contract
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: The funds for this post are available until 31 December 2028 in the first instance. To apply online for this vacancy and to view further information about the role, please click the 'Apply' button above
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computational design, Industry 4.0 integration, digital twins, and data-driven optimization to enhance manufacturing efficiency. Working closely with the NWCAM2 companies, this project aims to reduce waste, embed
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manufacturing sectors, from SMEs to large global manufacturers. For details, visit the MTC website . Entry requirements: A 1st or high 2:1 degree in computer science, manufacturing/industrial engineering, data
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computer simulations by developing fundamentally innovative and advanced protection strategies. To enhance the reliability and safety of low-voltage networks with a high penetration of power-electronic
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then need to provide the following information in the ‘Further Details’ section: A ‘Personal Statement’ (this is a mandatory field) - upload a document or write a statement directly in to the application
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is 2:1. Degree in psychology, public health, epidemiology or other health-related discipline. Mode of study: Full-time Start date: 1 October 2026 Additional Funding Information This project is fully
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training will be provided. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods. There is flexibility to tailor the research to your
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and in-depth information about their vision. This could lead to a paradigm shift for monitoring vision in patients with MS – enabling earlier and more reliable detection of vision-related manifestations
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geophysical and planetary models. IBM faces several such challenges in practice, many of which are currently approached using Foundation-Model (FM) surrogates. However, certain inverse problems arise in data