46 programming-"https:"-"https:"-"https:"-"CNRS"-"CNR-IRIB-PALERMO" 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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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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undertaking a specified range of activities within an established research programme and/or specific research project. The successful candidate will contribute to the project "Higher-Order Monad-based
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date: 12th May 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
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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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-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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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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positions available 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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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