97 engineering-computation-"https:" "https:" "https:" "https:" Fellowship research jobs in United Kingdom
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) Country United Kingdom Application Deadline 15 Dec 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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Job Description Position Details School of Metallurgy and Materials, College of Engineering and Physical Sciences Location: University of Birmingham, Edgbaston, Birmingham UK Full time starting
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selected for several prestigious talent programmes, including the Zhejiang University Talent Programme and the Yongjiang Young Scholars Initiative. For details, please refer to the link: https
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or supervising junior colleagues and contributing to team development. Desirable Prior experience in enzyme redesign, immune-related proteins, or computational protein engineering. Evidence of engagement with
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economy, renewable resources, and engineering innovation. Sustainable Society: climate resilience, behavioural science, adaptive governance, and equitable pathways to net zero. As our research grows, so
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workstream of mTBI-Predict (https://mtbi-predict.digitrial.com/), with an expected start date of 02/01/2026 and a fix-term duration of 6-months. The focus of each position is slightly different with one Fellow
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Nov 2025 - 00:00 (UTC) 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 related to staff position within
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Dec 2025 - 00:00 (UTC) 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 related to staff position within
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The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model that can
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The role The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model