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Field
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other field or laboratory instrumentation Apply appropriate behavioral, statistical, econometric, causal, spatial, machine learning, deep learning, computer vision, time-series, or mixed-methods
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, Spatial data analysis or GIS Whole-genome sequencing, phylogenetics, or phylodynamics Data management and reproducible analytical workflows Quantitative modeling and statistical inference. Applicants
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in computational methods and/or quantitative methods, primarily spatial analysis. Experience in supervising and managing research groups. Experience in conducting academic research related to analyzing
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statistical learning frameworks to decipher single-cell and spatial transcriptomics data, with the goal of uncovering cellular and tissue dynamics underlying cancer, inflammation, and tissue senescence
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well as collaborative work on the high-resolution structure of the podocyte slit diaphragm. The Oxford Kidney Pathology (Bull) Group have an interest in structurally informed analysis of kidney spatial transcriptomics
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, precision oncology, biomarker discovery, and translational cancer biology using cutting-edge computational, statistical, and multi-omics approaches. The Postdoctoral Scholar must be an expert communicator
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cancer progression in the West lab in the Department of Pathology at Stanford. Successful candidates will use a combination of spatial transcriptomics and highly multiplexed imaging to understand how
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large spatial and temporal scales. Experience in the conceptualisation and development of new methodological approaches for analysing and modelling species range dynamics, functional trait variation
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skills required. A strong quantitative background in statistics and/or spatial analysis. Experience with large publicly available datasets To apply, please submit the following materials: CV Cover letter
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, scATAC-seq, scTR-seq, and/or spatial transcriptomics). Previous experience in both Bioinformatics/Genomics and Cancer Biology is desirable. The Postdoc is expected to collaborate closely with experimental