287 data-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions in United Kingdom
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-molecule inhibitors. The postholder will use structural, biochemical and microbiological data to inform molecule design, generate and test hypotheses, and develop new synthetic methodologies. Alongside
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, the Cancer Treatment Outcome Programme at the Country Durham and Darlington NHS Trust. Experience conducting quantitative research including data collection, management, and analysis is essential. Experience
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Theoretical Physics. The successful candidate will work on the research project MakingAI: AI-Driven Integration of 'Messy' Data in Technical Art History, a close interdisciplinary collaboration between
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inventory data from mature and secondary forests with assessments from restoration sites. You will be expected to perform data analyses, author research papers, lead field expeditions to Brazil to fill key
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modeling. The role involves developing and implementing computational methods to integrate single-cell and spatial transcriptomics, proteomics, metabolomics, and metallomics data. Using advanced techniques
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to appoint a highly motivated and experienced postdoctoral research associate in computational materials chemistry. The position is funded by the EPSRC project “Data-driven design of organic mixed conductors
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, "INFORMED-AI" hub. More details can be found about the INFORMED-AI hub at. The post is fixed term for 24 months with the start data as May 1st , 2026, and the end as April 30th , 2028. The successful
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successful careers and build skills in achieving impact, so their findings can influence real-world change. Responsibilities include: • Supporting and conducting data collection across the research
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to Postdocs, Research Assistants, Research & Teaching Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information. About
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: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis