542 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "IFM" "IFM" "IFM" PhD positions in United Kingdom
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is offering multiple studentships for candidates from backgrounds spanning the physical and computer sciences. These students will develop core expertise in robotic, digital, chemical and physical
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, the student will be able to design and execute reactions, interpret complex analytical datasets, and summarise this information in a variety of formats (such as in presentations at group meetings
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imaging. Novel data workflows will be developed to allow parallel multi-element measurements. The project is interdisciplinary, combining instrument development, analytical chemistry, laser physics and
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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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MEng degree in Electrical and Electronics Engineering or Aerospace Engineering. To apply or for further information, please contact Dr Sharmila Sumsurooah Sharmila.Sumsurooah@nottingham.ac.uk Funding
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for this research studentship, please complete the online application form. For further information contact us today: T: +44 (0)1234 758540 E: study@cranfield.ac.uk
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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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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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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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knowledge for sustainable formulation design. Using different data-driven feature representations, AI foundation models will generate chemical embeddings to predict key physicochemical properties. Coupled