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Field
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and methodologies. Experience analyzing and interpreting spatial datasets using GIS software (e.g., ArcGIS). Experience conducting meta-analyses or applying meta-analytical statistical techniques
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for carrying out research in spatial immunometabolism, focused on the GC reaction. The lab is supported by the ERC and the post will also involve providing guidance to less experienced members of the research
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and validation in ex vivo human samples as potential biomarkers for diagnostic development. Further, they will learn other advanced molecular imaging techniques, including spatial transcriptomics with
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for carrying out research in spatial immunometabolism, focused on the GC reaction. The lab is supported by the ERC and the post will also involve providing guidance to less experienced members of the research
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: Statistical programming and data analysis using R, Python, or Matlab, Spatial data analysis or GIS Whole-genome sequencing, phylogenetics, or phylodynamics Data management and reproducible analytical workflows
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such as spatial transcriptomic and behavioral approaches. The selected candidate will be expected to drive the project, with the supervision and mentoring from Dr. Moyon. The ideal candidate should have
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society are required. Knowledge and skills of GIS and spatial studies are considered advantageous. To apply, please submit your application at https://careers.purdue.edu and include the following materials
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Development of next-generation technologies for spatial epitranscriptomics Discovery of novel m6A readers, repelled proteins, and regulatory mechanisms controlling gene expression in cancer We welcome
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hematopoietic stem cells. In addition, the lab is using novel, spatial omics approaches to more comprehensively understand transplant pathology. The main goal is to identify and validate novel biomarkers
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combines ribosome profiling, spatial and single-cell omics, immunocompetent mouse tumor models, patient-derived xenografts, and clinical samples to map translation dependencies across tumor evolution