29 evolution "https:" "https:" "https:" "Multiple" uni jobs at University of Sheffield
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and community-based support. This project builds on prior work using Natural Language Processing (NLP) to analyse electronic health records (EHRs) across multiple NHS Trusts. In a dataset of over six
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experimental approaches to explore fundamental evolutionary processes. While the majority of our research centres on the grass family (Poaceae), we welcome students with a broad interest in plant evolution
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Ton Application Deadline: 20 March 2026 Details Project Description Parasitic flowering plants have evolved independently from free-living ancestors multiple times, offering some of the most fascinating
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al. (2024) presented the analysis tools to perform a detailed investigation of such a flow. Therefore, the stage is set for this investigation to occur in multiple dimensions. In the first part of
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team with understanding in the relevant fields of Research, Project Management and Development. You will also aid in the development of portfolio and finance processes to improve performance. Policy
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, modelling, software development, and data analysis. This PhD project will focus on addressing the short baseline anomalies through measurements of key parameters which govern sterile neutrino oscillations and
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focus on the construction of detector modules for the new all-silicon Inner Tracking detector (ITk), part of which is being built in the University of Sheffield Semiconductor Detector Development Facility
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the development and refinement of Monte Carlo simulation generators to accurately model neutrino interactions with various target materials. Detailed comparisons of these simulations to data from existing neutrino
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team of multiple disciplines at the Integrated Manufacturing Group (IMG) to develop novel solutions for the high-value manufacturing sector, you will conduct research into specific technical fields
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to b-bbar. Additionally, the student will have the opportunity to assist with hardware development for the ATLAS tracker upgrade (for the HL-LHC era), gaining invaluable experience in experimental