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Employer and proud members of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly
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, Integration and Performance . This role will contribute to the development of various systems used in propulsion architectures. You will be working in test facility design, computational fluid dynamic modelling
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brain prioritises behaviour when animals face conflicting internal needs and changing environmental demands. This position forms part of the Wellcome Trust Discovery Award programme “Decoding Competition
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Deadline 6 Mar 2026 - 00:00 (UTC) Country United Kingdom Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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endemic countries. We are seeking to appoint a Research Fellow to join a research programme that applies advanced bioinformatic, statistical, and population genomic approaches to large-scale sequencing data
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The University of Nottingham are seeking to recruit a highly motivated and enthusiastic computational chemist for a Postdoctoral Research Associate/Fellow post within the research group of Professor
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-intensive Universities? As a Research Fellow in Computational Climate Politics you will work on the UKRI FLF project “Understanding normative change to address the climate emergency ”. This is a 4+3 grant
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Do you have a strong technical background in Machine Learning and Numerical Modelling? Are you interested in working with industry to develop Machine Learning methodologies and protocols needed to deliver resilient, interoperable and safe CO2 transport infrastructure in Europe? Carbon Capture...
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biggest challenges. We are seeking a Research Fellow in Liquid Metal Artificial Enzymes to join the School of Electronics and Computer Science (ECS) at the University of Southampton. The successful
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during