518 computational-physics-superconductor Postdoctoral research jobs in United Kingdom
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Field
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research in a collaborative and intellectually stimulating environment. Application Process You will be required to upload a supporting statement (setting out how you meet the selection criteria), a
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skills Desirable criteria Teaching experience Ambition to involve stakeholder communities in the research process. Experience in working with clinical records databases. Downloading a copy of our Job
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-geostrophic idealised model and use operational and bespoke Met Office model output for case study analysis. You will have : a PhD in atmospheric science, engineering, physics or mathematics (or a closely
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@rhul.ac.uk For queries on the application process the Human Resources Department can be contacted by email at: recruitment@rhul.ac.uk Please quote the reference: 0825-198 Closing Date: 23:59, 23 September
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to learn new techniques and apply them in an interdisciplinary research environment. Application Process Applications for this vacancy are to be made online and you will be required to upload a supporting
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physical health challenges, including anxiety disorders, trauma, somatoform disorders, chronic pain, psychosis, depression, antisocial personality disorders, childhood and adolescent disorders, emotional and
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information of what criteria will be assessed at each stage of the recruitment process. Further Information We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel
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-century material from mostly unpublished sources in UK archives; contribute to physical and digital exhibition curation; produce high-quality academic publications; contribute to academic symposia; and
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The Role and Department The successful candidate for this Research Associate position will join the Department of Engineering at Durham University. This is a unique opportunity to work at the forefront of hydrogen technologies and their integration to the energy systems. The successful candidate...
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly