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Field
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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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the direct supervision of a postdoctoral fellow to study breast cancer initiation and progression using a newly developed mouse model of breast cancer together with primary patient samples. You will
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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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single-cell or spatially resolved genomics methods Testing therapeutic interventions in models of ovarian and cardiovascular aging Designing in vivo lineage-tracing and labeling strategies paired with high
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mechanisms of fertility loss in females, the role of the microbiome in reproduction, susceptibility of hens to diseases including avian influenza, using organoid models to investigate reproductive function and
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at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied
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EV in vivo tracking studies, spatial transcriptomics, imaging mass-cytometry, micro-CT imaging and experimental fetal lung models to address key questions in lung development and repair. This position
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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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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large