237 evolution-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions in United Kingdom
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on research pertinent to the project. By scaling up data, compute and model size, large language models (LLMs) have gained an impressive and ever growing array of capabilities. The next phase of development
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Development) will offer you the tools you need to succeed in your research project and work towards your long term career ambitions. The University provides a working and teaching environment that is inclusive
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applicant will work on the project alongside Prof. Cheryl McEwan (Principal Investigator, Department of Geography, Durham University). The primary duties will be to contribute to the development
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expected to undertake and/or support interviews and qualitative analysis and will be involved in data collection from the museum collections, and all analysis, and publication development and conference
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energy potential, product yields and environmental impacts. In addition, the Research Associate will play a key role in the design and development of pelletiser and pyrolysis systems, supporting
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for Artificial Intelligence, and the Digital Futures Institute. The PDRA will also have access to research and career development from King’s Centre for Research Staff Development. The postholder will work closely
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. The broader goal of the overall programme, funded through the Wellcome Trust bioimaging technology development initiative, is to deliver multimodal datasets in an interoperable manner through open access
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environment will facilitate the development of new skills and the demonstration of creativity and leadership. Expertise in structure solution using powder and/or single crystal X-ray diffraction-based
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cavitation detection, imaging, and monitoring. You will be responsible for engaging in reactor design, construction, development, and characterisation. You will also be expected to integrate a cavitation
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funding. You will be responsible for the development of novel acquisition, reconstruction, image analysis and/or modelling methods for cerebrovascular magnetic resonance imaging (MRI) to improve