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Field
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position will contribute to the research programme of the recently founded "AI Hub in Generative Models", a research consortium funded by EPSRC. The goal of the programme is to do research in the area of
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causing damage to the stomach epithelium? You will work with a mouse gastric organoid-derived tissue model to explore this in a hologenomic framework. You will have opportunities to participate in relevant
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. It is essential that they are knowledgeable in current behaviour change science and techniques and specifically have experience of designing topic guides and administering in-depth, semi-structured
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into the genomics of population structure and speciation in the Malawi cichlid genus Labeotropheus. This post will build on past work in which over 1000 samples of Labeotropheus from multiple species/populations have
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Classification Title: Post Doc Associate Classification Minimum Requirements: A Ph.D. in biomedical informatics, computer science or a related field. Working experience with large language models
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to state-of-the-art facilities. Join us in our mission to advance our understanding of alveolar development, homeostasis, regeneration, and pathology. Your valuable contributions will significantly impact
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with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
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to therapies and vaccines against human diseases. We are a team of highly interactive investigators that have expertise in immunology, molecular biology, virology, microbiology, structural biology, computational
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behavior of pests, their response to pesticides, including physiological and genetic mechanisms of resistance, innovations in applied pest management research (e.g., molecular, computational modeling
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– with prior experience working with administrative data. Advanced skills in database management (i.e., SPSS,Excel) as well as advanced analytic approaches (i.e.,multivariate statistics) are required. SQL