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are particularly relevant. The developed numerical models will leverage a large suite of in-situ and remotely sensed observational datasets, spanning from subsurface to atmosphere. You will have experience
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a curated catalysis database supporting AI-driven catalyst design. Release data, workflows, and benchmarks via the Materials Project and Genesis AmSC; publish in peer-reviewed journals and present
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Python, R or a similar scientific programming language, with a focus on reproducibility and open-source best practices Demonstrated experience in geospatial data analysis and the management of large
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analyses in large-scale multimodal datasets (imaging, phenotypical data, genetics data) Author scientific publications and contribute to grant proposals Supervise trainees and maintain research
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; ● Experience compiling, processing, and analyzing large spatial fisheries datasets and/or fishing fleet behavior (via e.g., AIS data) in management contexts; ● Demonstrated understanding of how insights from
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on reproducibility and open-source best practices. Demonstrated experience in geospatial data analysis and the management of large, gridded meteorological or environmental datasets (e.g., NetCDF
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, and implement scalable solutions that work across boundaries—connecting science with policy, data with decisions, and ambition with action—to create pathways toward a future where environmental and