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-imaging tumourigenesis within the optically translucent Drosophila pupa. 2: Establishing RNA sequencing protocols for implementation with a Drosophila tumour models (bulk, single-nucleus sequencing
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analysis and statistical modeling. Experience working with large, complex, and multi-dimensional datasets. Experience with spatial analysis and geospatial data integration, including use of GIS tools (e.g
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model organisms in their work and are pushing into emerging model and non-model organisms that are proving uniquely valuable in particular studies. To learn more about our department, https
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statistical models. Within the Polarity, Division and Morphogenesis team, the candidate will work closely with biologists and physicists to develop approaches integrating spatial transcriptomics, cell dynamics
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potential of three complementary mammary cell models: healthy epithelial MCF-10A cells, non-invasive tumor MCF-7 cells, and highly invasive MDA-MB-231 cells. A macroscopic phenotypic screening — encompassing
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. The project will construct the first-ever Spatial Integrated Assessment Model of the global water cycle. Combined with global spatial data on economic activity, water usage, and atmospheric evaporation
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, progression, and therapeutic response. This research is fundamental to advancing our knowledge of cancer and improving patient outcomes. See further information at the lab webpage: https://odin.mdacc.tmc.edu
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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machine learning (ML) approaches offer a powerful framework for modeling complex catalytic materials with near ab initio accuracy while enabling simulations at significantly larger spatial and temporal
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immediately, depending on visa status and requirements. Group or Departmental Website: https://med.stanford.edu/matteo-mole.html (link is external) https://www.devo-evo.com (link is external) How to Submit