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identification, and you will support onward analysis of these datasets. This is a full-time position working 37 hours per week on a fixed-term basis for 12 months due to funding. For more information on Biological
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trained artifical intelligance model for deep sea species identification, and you will support onward analysis of these datasets. For more information on Biological and Marine Sciences please visit our
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More Jobs from This Employer https://main.hercjobs.org/jobs/22073898/research-associate-i-spatial-data Return to Search Results
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-dimensional datasets. Experience with spatial analysis and geospatial data integration, including use of GIS tools (e.g., ArcGIS, QGIS) to link, harmonize, and analyze datasets across spatial scales. All
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to the Dillon Lab’s multidisciplinary research program evaluating the sustainability of animal-source foods. The position combines spatial analysis, livestock systems modeling, and environmental assessment
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implement analysis pipelines in several broad areas of computational biology including analysis of experimentally generated datasets in basic research, and analysis of patient-derived genomic data from UCSF
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advancements in spatial bioinformatics. Assist with the epigenetic analysis performed within CIMP and MBH lab. What we offer Opportunity to become a leading-edge expert in spatial bioinformatic, being
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functional genomics, supported by extramural funding. Develop novel technologies and/or apply advanced spatial analysis to study tissue microenvironments in health and disease. Engage in undergraduate
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implement end-to-end data pipelines, develop AI models to extract structured intelligence from unstructured social media data, build spatial risk models, and translate these outputs into a secure, user-facing
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Midlands Graduate School Doctoral Training Partnership | Loughborough, England | United Kingdom | about 2 months ago
and ecological change. The research combines critical advanced quantitative spatial analysis with qualitative and participatory approaches to connect structural inequalities with lived experience