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Field
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science LCN and UCL Physics and Astronomy are at the forefront of research into complexity in materials science, particularly in the area of emergent properties in strongly correlated systems. Particularly
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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We are seeking two post-doctoral candidates for research in the area of food and maritime supply chain resilience analysis. The responsibilities are as follows: (1) Develop resilience models
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for: Analysis of bulk and single cell RNA-sequencing, spatial transcriptomics, and proteomics datasets Integration of experimental model data with public and clinical datasets Statistical modeling and survival
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Closing date: 17th September 2026 Background Applications are invited for a 2 year full-time Post Doctoral Research Fellow with bioinformatics expertise to contribute to the analysis of large datasets
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will develop innovative methods used to detect emerging pathogens earlier, understand who is most at risk, and translate the complex multi-source data into real-time, evidence-based decisions and
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molecular microbiology methods (e.g., nucleic acid extraction, qPCR, dPCR) and quantitative data analysis Fieldwork experience, including environmental sampling Strong written and oral communication skills
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, and delivery of health services. The programme focuses on public health and clinical service innovation in an increasingly complex and connected world. We are seeking a Research Fellow/Senior Research
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programming (e.g. Python, MATLAB or R) Experience applying advanced analytical techniques, including machine learning, to complex datasets Experience with neuroimaging analysis (MRI/fMRI) is desirable Strong
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complexes Analysis of target-ligand structures to inform compound design Qualifications and personal qualities: The applicant must hold a Norwegian PhD or an equivalent degree within structural biology or