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novel biomarkers by integrating proteomics, metabolomics, and genomics / transcriptomics data with machine learning techniques. The position is to be filled starting November 1, 2025, either full-time or
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organoids will be plus. Dry lab: Highly motivated candidates with a PhD/MD degree in bioinformatics, genome science, systems biology, biomedical informatics, computational biology, machine learning, data
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a role model and fostering an inclusive working culture. Person Specification PhD, or close to completion, in a relevant, quantitative field, e.g. meteorology, machine learning, climate science
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, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded methodology project
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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to most destinations in the world. Interested candidates are encouraged to send a brief cover letter, CV and the names and contact information of 3 references to: Prithu Sundd, PhD - psundd@versiti.org
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information, disability, age, ancestry, military service, protected veteran status, or other characteristics protected by law.