46 computer-programmer-"https:"-"FEMTO-ST"-"Inserm"-"https:"-"https:"-"https:" Fellowship positions at University of Birmingham
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2026 Background To create and contribute to the creation of knowledge by undertaking a specified range of activities within an established research programme and/or specific research project
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organising of the research programme and/or specific research project • Co-ordinate own work with others to avoid conflict or duplication of effort • Knowledge of the protected characteristics
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delivering the research goals of the group Conduct a research program, defined in agreement with the group Contribute to writing bids for research funding (including facility access) Analyse and interpret data
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activities within an established research programme and/or specific research project. Role Summary • Work within specified research grants and projects and contribute to writing bids • Operate within area of
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future of formulated polymers. We are seeking a Research Fellow in Computational Chemistry and AI/Machine Learning to advance the state of the art in understanding the degradation and biodegradation
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-institutional strategic national research programme dedicated to using data to transform our understanding of cancer risk and enable early interception of cancers. It represents a major, multi-million-pound
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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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Future Leaders Fellowship programme at the School of Physics and Astronomy, University of Birmingham. The position is available for up to three years (until May 2029). The project focuses on nanoscale
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undertaking a specified range of activities within an established research programme and/or specific research project. Role Summary Work within specified research grants and projects and contribute to writing
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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