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about to submit) a PhD in Physics, Mathematics, Computer Science or related discipline in the Physical Sciences. No prior knowledge of biology is required but willingness to work at the interface
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to contribute to student supervision and research development, as well as service and engagement roles and activities. Key responsibilities will include: Research: Contribute to and develop a research program
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wavefunction calculations on high-performance computing clusters. You may also: Possess a PhD (or near completion) in Theoretical Quantum Chemistry or a related discipline. Have experience in developing and/or
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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PhD Research Fellow in Chemistry-focus on Experiments in Chemical Kinetics and Heterogeneous Catalys
-erklæringen var sist oppdatert 10.08.2025 Hva er en cookie? En cookie er en liten datafil som lagres på datamaskinen, nettbrettet eller mobiltelefonen din. En cookie er ikke et program som kan inneholde
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College of Engineering, Design and Physical Sciences Location: Brunel University of London, Uxbridge Campus Placement: Fixed term (12 months), Full–time, up to £40,757 per annum Brunel University
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for a PhD Research Fellow in Hydrogen Safety at the Department of Physics and Technology (IFT). The position is for a fixed-term period of four years, including one year allocated for career-promoting
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components; patient care, education and research that together enhance our contribution to society. Required Qualifications* PhD in bioinformatics, computational biology, systems biology, statistics, physics
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for drug discovery. Publishing peer-reviewed research and contributing to industrial software tools. About You To be successful in this role, you will have: A PhD in machine learning, computer
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Computer Science, Mathematics, Physics, Applied Economics, or a related quantitative field. Skills and Knowledge: Knowledge of scientific computing, data assimilation, and machine learning frameworks. Proficiency in