60 computer-programmer-"https:"-"CEA-Saclay" "https:" "https:" "https:" "https:" "https:" "Dr" "UCL" Fellowship positions at University of Birmingham
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resources effectively Understanding of and ability to contribute to broader management/administration processes Contribute to the planning and organising of the research programme and/or specific research
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significant experience in developing innovative postgraduate education programmes in this field. The University of Birmingham is the UK lead for SAFER‑MH, a major Wellcome Trust‑funded programme aimed
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social decision-making, as part of a Jacobs Foundation award to Dr. Patricia Lockwood A 2-year postdoctoral position (with possibility of extension subject to funding) is available from 11th April 2026
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to communicate complex information clearly Ability to assess resource requirements and use resources effectively Contribute to the planning and organising of the research programme and/or specific research project
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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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participate in the research culture of the department as per the information above. Please send your statement to Dr Michaela Giebelhausen via email: m.giebelhausen@bham.ac.uk The deadline for applications
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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