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to sustainable technology developments - interest in interdisciplinary research - ability to write and communicate professionally in English - ability to collaborate tightly within a
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Master’s degree in physics, chemistry, materials science, chemical engineering, or a related field who are excited about applying machine learning and data science to real-world materials challenges
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full time, internal student (unless approval for part-time and/or external study is obtained). You must commence your degree by 1 August 2026. Have a background in civil engineering, and: first class
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receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to increase each year. Computational haemodynamic modelling provides a
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conduct and interpret tomorrow’s fusion experiments? Do you have a background in plasma physics, computational physics, inverse problems, or tomography? Then this PhD opportunity at the Technical University
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Caltech is a world-renowned science and engineering institute that marshals some of the world's brightest minds and most innovative tools to address fundamental scientific questions. We thrive
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, Chemistry, Physics, Engineering, Mathematics, Computer Science, Data Science, Machine Learning or Artificial Intelligence a minimum 2:1 undergraduate degree (or equivalent) Excellent spoken and written
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the ‘Sustainable Transitions – Leverhulme Doctoral Training Programme’ at the University of Essex. Only UK domiciled applicants who meet the following criteria may apply: Not already have a Master’s degree. And Be
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Project Overview This is an opportunity to conduct fully funded interdisciplinary research under the ‘Sustainable Transitions – Leverhulme Doctoral Training Programme’ at the University of Essex
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with modern machine learning. You will work on extending data-driven models with process-informed constraints and novel data integration strategies. The position is embedded in the Computational