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Field
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large
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models. We focus on creating organoids with precise cell types, architecture, and functions that closely mirror their in vivo counterparts. Our mission is to harness these advanced models to unravel
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, particularly single-cell RNA sequencing, spatial transcriptomics, and related approaches. Our research group uses single-cell genomics to study immunity at mucosal sites, with a focus on the lungs and
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advanced contextual spatial and temporal modelling approaches. You will work on combining multi-source data such as field observations, laboratory measurements, environmental data, and drone- and satellite
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advanced contextual spatial and temporal modelling approaches. You will work on combining multi-source data such as field observations, laboratory measurements, environmental data, and drone- and satellite
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model for communities worldwide facing similar 21st-century challenges. This is a fixed-term position for one year with the option to renew for a maximum of two years. UT Austin offers a competitive
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in spatial analysis and data visualization Computer programming skills relevant for data manipulation and analysis Experience with creating and using complex data-driven analytical models using R
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a broad spectrum of research activities, utilizing state-of-the-art instrumentation at beamline 32-ID. The use of holotomography with PXM enables full-field imaging with spatial resolutions ranging
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model for communities worldwide facing similar 21st-century challenges. This is a fixed-term position for one year with the option to renew for a maximum of two years. UT Austin offers a competitive
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applicant must have professional proficiency in English (written and spoken). Desirable qualifications: Prior experience in working with economic models of migration, residential choice models, and/or spatial