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maintain algorithms in biomedical image analysis pipelines. Validate algorithm performance on novel datasets. Analyze spatial transcriptomic data. Perform statistical analyses on data extracted form bio
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Primary Work Address: 19700 Helix Drive, Ashburn, VA, 20147 Current HHMI Employees, click here to apply via your Workday account. TLDR: Build the data backbone for the next era of AI-powered spatial
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methods for forensics, life sciences, topological data analysis, spatial statistics, and computational statistics). The department values an informal atmosphere with strong collegial support, openness, and
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Oldenburg Oldenburg, Niedersachsen | Germany | 2 days ago
science, or a related subject Experienced in acoustic propagation modelling, signal processing, and data mining A strong background in programming, statistical analysis, and spatial modelling and mapping
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crane. The successful candidate will build reproducible machine learning pipelines, integrate detections into spatial ecological models, and generate conservation-relevant outputs for regional partners
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the causal circuits driving the development and the functional specialisation of the largest macrophage population in the body: the liver-resident Kupffer cells. You will next develop Spatial CRISPR screens in
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methods to biological and clinical research datasets. Primary responsibilities include analyzing bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics data; developing and maintaining reproducible
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-throughput biological datasets Experience with analysis of proteomics or spatial transcriptomics experiments is a plus Sound statistical knowledge and fluent programming skills using R/Bioconductor and/or
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statistical and epidemiological methods Working experience with data analysis software, such as Stata, SPSS, R, or Mplus Experience in spatial and temporal data analysis using GIS, including RStudio/Python
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learn about carcinogenic mutagens (https://www.biorxiv.org/content/10.1101/2023.12.06.570467v1 ), while studying the spatial genetic heterogeneity of tumors tells us about the tumor mode of growth (https