44 computer-"https:"-"https:"-"https:"-"https:"-"KU-LEUVEN" Fellowship positions at University of Birmingham in United Kingdom
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Job Description Position Details School of Computer Science Location: University of Birmingham, Edgbaston, Birmingham UK Full time starting salary is normally in the range £36,636 to £46,049 with
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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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Job Description Position Details School of Computer Science, College of Engineering and Physical Sciences Location: University of Birmingham, Edgbaston, Birmingham UK Full time starting salary is
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Postdoctoral Research Associate to join the Research Group of Dr Adam Michalchuk, funded by the US Air Force Office of Scientific Research. The position will be to develop and use computational models to explore
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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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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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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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-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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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