174 phd-mathematical-modelling-population-modelling Postdoctoral positions at University of Oxford
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data to build hypothesis and test them in laboratory models. You will contribute ideas for new research projects, collaborate in the preparation of scientific reports and journal articles and act as a
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Prof. Luigi Rizzi (Collège de France), seeks to investigate the acquisition of French from a cartographic perspective, employing the Growing Trees model developed by Friedmann, Belletti, and Rizzi, and
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We are seeking to appoint a highly motivated Postdoctoral Researcher to join the research group of Professor Ignacio Melero, MD PhD, at the Oxford Centre for Immuno-Oncology within the Nuffield
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these bioinformatic experiments. Access to a high-performance computer will be provided. The candidate must be capable of generating complex molecular compound models in silico and using current molecular dynamic
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proteome in heart-specific cell lines and primary tissue. It will utilize disease model systems to characterize unique cell surface signatures for cardiomyocytes, coronary endothelial cells, and fibroblasts
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good understanding of the relevant basic theory, skills in data analysis and numerical modelling, and a strong research track record. Please direct enquiries about the role to: Only applications received
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Oxford Population Health (Nuffield Department of Population Health) is home to world-renowned population health research groups and provides an excellent environment for multi-disciplinary research
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with cutting-edge models and technologies—including patient-derived glioblastoma organoids, CRISPR-based screens, mass cytometry, and advanced microscopy—to dissect these complex biological processes
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for the provision of research support for the ARC project on risk assessment tools in psychiatry, and particularly in child and adolescent psychiatry. About You You will have or be close to completing a PhD/DPhil in
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challenge. We seek a senior computational biologist to apply these extensive in-house datasets toward the development of novel, domain-tailored machine-learning models and analytical methods. You will explore