128 postdoc-computational-fluid-dynamics Postdoctoral positions at University of Oxford in United Kingdom
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of the research group, including postdocs, research assistants, technicians, plus PhD and project students. You must have: A relevant PhD/DPhil (or be close to completion), together with relevant experience in
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inorganic/organic chemistry (including synthesis), electrochemistry and physical chemistry (including spectroscopy, modelling and reaction dynamics). You will be a well-organised and self-motivated scientist
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to independently plan and manage experimental test campaigns. You should be motivated to work in a dynamic environment, have the ability to self-manage, and be driven to support research within the wider group
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research projects and work independently while also collaborating effectively within a dynamic, multidisciplinary team, balancing your own work with supporting the smooth running of the group. This post is
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laboratories and equipment, including GPCs, DMA, rheometer, TGA, DSC, UTA, impact pendulums, compression moulder, injection molder and compounder. You will join a dynamic team of other experts working in
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Medicine provides a vibrant and dynamic environment for researchers, offering state-of-the-art facilities and a multidisciplinary team approach to cardiovascular research. This opportunity is particular
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About the role We seek a dynamic and self-motivated individual with expertise in the ‘Global Renaissance’ (defined temporally as c. 1400 to c. 1650) to facilitate research and engagement work in
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crystallization dynamics on defect formation, optoelectronic properties, and stability in perovskite thin films and devices. This project will advance the fundamental understanding of the impact of deposition
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to develop a program of work investigating how brains use internal models of task and world structure to enable flexible goal-directed behaviour. The experiments will involve recording and/or manipulating
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, economics, and other areas of computational social science; • AI scientists for natural science, integrating LLM agents with simulation and, where appropriate, robotic experimentation (e.g., automated “dry