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SPINTEC fabrication facilities. As such, this thesis will be stationed mostly at SPINTEC in Grenoble, the PhD program being registered at Université Grenoble Alpes. You will also be part of the Vertical
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education. You must meet the requirements for admission to the doctoral programme in Materials Science and Engineering, https://www.ntnu.edu/studies/phmt . Your academic degree must contain appropriate
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Supervisors: Professor Richard Hague1 , Professor Chris Tuck1 , Dr Geoffrey Rivers1 (1 Faculty of Engineering) PhD project description: Inkjet printing allows multiple materials to be 3D-printed
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Fees for 4 years (Full time), £12,000 stipend for 3 years We are pleased to invite applications for a funded PhD studentship exploring how a curated Arts Public Programme can address pressing issues
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PhD Position - Organic Electrosynthesis: monitoring of reaction transients with real-time techniques
career in science and/or industry A comprehensive training program, including soft skills, along with an flexible working hours and various opportunities to reconcile work and private life Flexible working
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Funding: School of Computer Science Studentship consisting of the award of fees, together with a tax-free maintenance grant of £20,780 per year for 3.5 years. Lead Supervisor’s full name & email
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Department: Materials Title: Predicting and Improving the Quality of Recycled Plastics Using Advanced Metrology and Data Science Application deadline: 15/09/2025 Research theme: "Materials
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diverse backgrounds (e.g., economics, engineering, computer science, information systems, etc.), united in pursuit of sustainable solutions that positively impact and shape a low-carbon economy and society
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(or equivalent) in a biomedical science. Experience in neuroscience and/or immunology is desirable. Project key words Retinal imaging, data-analytics, computer vision, big data Funding The studentship, funded by
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. The start date is October 2025. Are you passionate about applying computational science to real-world engineering problems? Do you want to develop digital twins of materials that can predict performance and