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Field
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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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quantitative ecology, applied statistics, or a related field with strong background in statistics and model development. Experience with R and analyzing spatial datasets. Ability to apply quantitative methods
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with or managing research activities, and preparing reports. Strong skills in statistical analysis and handling large spatial and temporal datasets. Good communication skills for collaboration within
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. Preferred qualifications ● Fluency in fisheries and/or bioeconomic modeling, with experience in spatial fisheries ecology or management strongly preferred; ● Advanced understanding of the biology and
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energy supply systems, multi-objective and stochastic optimization, advanced statistical analysis, and data visualization. This position offers the opportunity to work with a multidisciplinary team of
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for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell
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to help define a new frontier at the interface of spatial biology and therapeutics. The scholar will lead spatial-biology driven projects to identify novel spatial biomarkers, therapeutic targets, and
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-based microarrays, immunohistocheistry, spatial transcriptomics and molecular analysis. The candidate is expected to be able to summarize data for inclusion in grant proposals and manuscripts; prepare
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experiments. Develop and optimize sample preparation protocols including flash freezing/ chemical fixation of samples and cryo-sectioning. Process and analyze complex spatial metabolomics datasets. Lead
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received after the review date will only be considered if the position has not yet been filled. Position description The Spatial Climate Solutions Lab at the University of California, Santa Barbara, is