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interdisciplinary team from electrical, mechanical, and chemical engineering, as well as physics and chemistry, with the intention to bring promising new 2D materials out of the laboratory and into the market-place
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deformation. Responsibilities Develop scientific machine learning methods in close collaboration with team members specializing in experimental techniques and materials science. Utilize unique experimental data
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topic of this project and who have academic training in one or more of the following areas: A master’s degree in chemistry, chemical engineering, materials science, physics, or a related discipline
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publications and project reports Support in establishing the institute Your Profile: Master`s degree in physics, chemistry, materials science, chemical engineering, or a related discipline Extensive knowledge
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regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class
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without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any
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£20,780 per year, and includes a 3-month fusion engineering CDT training programme as part of the 2026 Cohort. This project is co-supervised by Dr Chris Hardie from UKAEA. The UoB Materials for eXtremes
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USNH Employees should apply within Workday through the Jobs Hub app The Department of Civil and Environmental Engineering in the College of Engineering and Physical Sciences at the University
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for: A first or upper-second-class degree in an appropriate discipline such as, materials science and engineering, nuclear engineering, fusion energy, chemical engineering, physics, chemistry, mechanical
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spectroscopy data and AI, to automatically identify textile fabrics with high accuracy in real-world sorting conditions by (1) defining optimal spectral bands, spatial resolution, and acquisition speed; (2