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developing next-generation AI methods for healthy climate adaptation. The position will focus on building and evaluating foundation models for large-scale spatiotemporal health and environmental data. Our team
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rodent (rat/mouse) models. The Research Fellow will work closely with a multidisciplinary team to design, build, and test sensor systems for recording neural and physiological signals. Responsibilities
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interdisciplinary approaches that combine advanced microscopy (confocal, electron, in vivo multi-photon), viral vectors, protein engineering, mouse models, and multi-omics analyses. For further information on the lab
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), library research (Pubmed searches, systemic review methods), and statistical analysis (data visualization, descriptive analyses, time series analyses, latent modelling, multilevel modelling); Project
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sequence-function landscapes, using a combination of empirical data and ML methods (e.g. transformer models). One focus of this work will be on B-cell receptor evolution. Experience in applications of modern
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mouse models Experience harvesting tissues and performing in vivo and ex vivo tissue imaging (e.g., organotypic slice cultures, intravital imaging, and/or ex vivo tissue imaging) Candidates with both
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to candidates with prior experience in one or more of the following areas: ecological (population) modeling, geographic information science, comparative genomics, chronobiology, insect rearing, and/or ecological
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mission to ignite positive changes in the quality of health across the world. The Department of Epidemiology is seeking 2-3 postdoctoral researchers for projects related to mathematical modelling and
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analyses, latent modelling, multilevel modelling). Data Cleaning and Management. Engages both independently and under supervision in data management and data cleaning and organization in large (N~100,000
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system transformation. Fellows will engage in rigorous empirical, computational, and theoretical research that integrates engineering models of power systems with modern econometric and economic analysis