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analysis tools for the large data stream of HTP phenotyping for assessment of abiotic and biotic stress tolerance (e.g. determine extent of damage to a disease or functional response to humidity); also
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THIS VACANCY IS OPEN TO INTERNAL CANDIDATES ONLY About Us We are seeking a highly motivated and experienced researcher to work on the large-scale programme and associated projects of the Data
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-grade codebases, including data pipelines and analysis workflows. Developing and deploying AI agents and large language model–based tools for tasks such as document processing, information extraction
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effectively and efficiently with large scale electronic healthcare data (such as CPRD, MINAP, HES) applying advanced statistical modelling and causal inference techniques. What we offer in return 26 days
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have experience developing and running large numerical models and analysing complex datasets? Would you like to work with a world-leading team at Leeds to connect molecular-scale insight to cloud-scale
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1-year fellowship in Shoebill Conservation Genetics with support for a further 3-year PhD fellowship
Your position The shoebill is an iconic bird species endemic to central eastern Africa, with the southernmost breeding population remaining in Zambia. Despite its large distribution area, the bird
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. The University of Leeds is part of a large consortium of 24 partners from 7 European countries, consisting of leading international universities, research organisations and leading international energy companies
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will engage in implementation science research, analyses of large claims and electronic health record data sets, pharmacy data, social determinants of health, for epidemiologic and pharmacoeconomic
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communication skills in English, with demonstrated experience in scientific writing - essential for data analysis and communication with stakeholders At least one first-author publication in an internationally
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degradation understanding, especially for micro-mobility systems. It also looks at building the capability of management of large volumes of multimodal data by designing and validating efficient and scalable