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Field
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learning, deep learning, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific
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transcriptomics and genomics datasets. The goal is to identify molecular and spatial signatures of disease progression both in human samples and in experimental models of diabetes. Qualifications: PhD in
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contributing to specifically the area of handling spatial data to assess the distribution of several soil properties and fungal communities using samples collected from multiple habitats and land use types at a
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fluorescence-lifetime detection (Fast-FLIM) and temporal focusing. This instrument will deliver quantitative, sub-second imaging of live three-dimensional cell-culture and organoid models, advancing fundamental
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model with a farm model Run scenario analysis to identify management practices with the largest mitigation potential, both spatially and temporally Coordinate and contribute to dissemination
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sensing provides large-scale and consistent observations, in-situ data collection remains a vital component for ground-truthing, model calibration, and validation of automated monitoring. However
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of the microbiome in reproduction, susceptibility of hens to diseases including avian influenza, using organoid models to investigate reproductive function and response to pathogens. The lab uses state-of-the-art
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; or 5) single-cell RNA/ATAC sequencing analysis and spatial transcriptomics. Candidates must hold a PhD degree. To apply, please visit: https://recruit.apo.ucla.edu/JPF10108 The University of California
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datasets. Your focus will be on implementing and training generative models to decompose cylindrical projections. You will solve and refine the structures from the resulting decomposed data. You will map
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. Contribute to the supervision of master and PhD students. Qualifications: Ph.D. in Earth Sciences, Remote Sensing, Physics, Applied mathematics, or related field. Strong background in land surface modeling