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Quantification and spatial analysis of immune biomarkers (e.g. TILs, immune subpopulations) Development of AI pipelines for automated analysis of whole-slide images Predictive modelling of relapse and metastasis
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
Developer will report to the Director of the Center for Environmental Modeling for Policy Development, within the Institute for the Environment. The Research Associate will support software development
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TLDR: Build the data backbone for the next era of AI-powered spatial biology. Please include a cover letter with your application detailing your qualifications and experience for this position
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(http://vanallenlab.dana-farber.org/) to work on the analysis of new datasets generated in the context of multiple clinically oriented cancer sequencing projects in order help advance efforts
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Oldenburg Oldenburg, Niedersachsen | Germany | 2 months ago
CCAMLR meeting: the expiration of Conservation Measure (CM) 51-07, which previously mandated that the maximum allowable catch of 620.000 t be spatially distributed among subareas 48.1 to 48.4. With
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://past2future.org ) with 24 partners. It has recently been funded by the EU horizon program. The aim of the position’s subproject here is to use latest spatially downscaled climate model results for the past (mainly
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LMU Munich. The group is located within the Institute of Clinical Neuroimmunology (https://www.neuroimmunology-munich.de/ ). The professorship of Systems Neuroimmunology is part of the Cluster
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structured data sets within a joint species distribution modelling framework. For more information, see: https://www.helsinki.fi/en/researchgroups/research-centre-for-ecological-change . The Department
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Cancer Institute seeks a Research Fellow to focus on next-generation, translational pathology studies who will work within the Wolpin-Nowak laboratory (https://labs.dana-farber.org/wolpin-nowaklab/) as
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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology