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areas of sustainable intensification of agriculture, climate mitigation and adaptation, livestock systems, and healthy and sustainable diets, providing analysis and insights aimed at helping to shape
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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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analysis pipelines and to integrate imaging data into computational models. It is an opportunity to work with research groups across the Cell-Matrix Centre and the Bioimaging Facility. The appointee will be
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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proteomics. Experience with spatial data analysis including multiplex IHC and CODEX. Familiarity executing bioinformatics pipelines in local UNIX/Linux environments and cluster execution (LSF). Proficiency in
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stronger. We seek employees who bring different and innovative ways of seeing the world and solving problems. The Nelson research group (https://research.fredhutch.org/peternelson/en.html at the Fred Hutch
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across different spatial and temporal scales, from building-level energy demand to district-scale interactions and their integration with wider energy networks. PhD Position in Hierarchical Graph Neural
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such as R, SAS, ArcGIS, SQL, Python and AI tools. Conducts geospatial and epidemiologic analyses relevant to the catchment area to assess cancer outcomes, spatial patterns, temporal trends, and disparities
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-index tomography experimental setup, including optical alignment, calibration, and performance evaluation. Conduct quantitative performance analysis of the system, including spatial resolution, phase
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successful candidate will have one or more of the followings: Expertise in spatial transcriptomics (single-cell RNAseq). Expertise in imaging and image analysis. Experience with genetic engineering (plasmid