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observed in Drosophila larvae. This interdisciplinary project combines biology, neuroscience, and computational modelling to understand how the larva’s body’s physical properties influence its motor control
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researchers as needed. Qualifications PhD Degree: A Ph.D. in Statistical Genetics, Computational Biology, Bioinformatics, Epidemiology, or a related field. Statistical and Programming Skills: Proficiency in
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exciting opportunities for machine learning to address outstanding biological questions. The PhD student to be recruited will be working on the development of machine learning methods for single-cell data
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exploited by the tumorigenic process. Candidate profiles We are seeking highly motivated candidates with: A PhD (or near completion) in cancer biology, immunology, cell/molecular biology, or related fields. A
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) are the sentinel of the immune system. DCs are developmentally and functionally heterogeneous and encompass multiple subsets including XCR1+ IRF8+ DCs, and a variety of IRF4+ DCs (DC2As, DC2Bs, DC3s) and
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functionally heterogeneous and encompass multiple subsets including XCR1+ DCs, and a variety of IRF4+ DCs (DC2As, DC2Bs, DC3s) and plasmacytoid DCs. DCs are short-lived cells continuously developing from
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, including Titan Krios and Glacios microscopes, a fully equipped crystallography platform, advanced computing clusters, proteomics and BSL-2/3 imaging facilities. The institute provides numerous training
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molecular signatures and potential therapeutic targets We are looking for a highly motivated and enthusiastic candidate with interest in neurovirology. Ideal applicants hold: A PhD in cell and molecular
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; developing our partnership programme with industry; contributing to a quality management system; and the organization of webinars and other dissemination activities, including publications. Support from
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understanding of how neural coding and speech perception are degraded in individuals with Auditory Neuropathy Spectrum Disorders (ANSD) [1]. The project leverages physiologically-informed computational models