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, or equivalent) conferred by the start date. 2. Proficiency in R/Python. 3. Experience with spatial proteomic/transcriptomic data analysis. 4. Growth mindset and motivation to advance our understanding of breast
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, inferential and spatial analysis, as well as integrating machine learning techniques, and leading and drafting manuscripts. We will support and encourage the candidate’s independent research interests
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complex endometrial models and optimizing in vitro implantation assays. Culturing human embryos and generating stem cell-based embryo models. Tissue sectioning for advanced spatial transcriptomic analysis
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-on experience in gastrointestinal physiology, immunology, inflammatory diseases, or stem cell biology. Experience in molecular and cell biology, flow cytometry, FACS cell isolation and analysis. Experience
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gain training in 2D/3D spatial multi-omics, single-cell spatial pharmacology, AI-enabled tissue analysis, and translational cancer biology, with access to large, high-quality, in-house spatial datasets
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experimental platform for spatial multi-omic analysis of biological tissues. Our lab builds biological measurement infrastructure—engineering systems that standardize how information is extracted from complex
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fields. Applicants should have a range of quantitative skills including graduate-level knowledge of spatial data analysis, data management, statistics, and machine learning/computer vision. Advanced
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at the time of application. Experience in molecular and cell biology techniques, preferably in single cell sorting (FACS), scRNA-seq, spatial transcriptomics and very strong multi-omic data analysis skills