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receive advanced training in: Statistical genetics and multi-omics integration Deep learning for regulatory genomics Single-cell and spatial transcriptomics Large-scale data engineering Training is highly
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, Geography, Spatial Ecology, or a closely related discipline. With no more than five years post receipt of PhD. The ideal candidate will have demonstrated experience conducting independent marine spatial
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Computational Sciences or similar. You have strong expertise on analyses of biology-related large datasets. Expertise in single-cell and spatial data analysis, spatial statistics and annotation is an advantage
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-environment-climate public goods (AECPG). By combining insights from results-based, collective and spatially targeted schemes with novel financing and robust monitoring, REWARD seeks to build scalable models
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, spatial and hydrologic modeling, statistical analysis, high-impact technical publishing, and collaborative proposal development. In support of NRRI’s mission to deliver applied science and sustainable
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, viability and biological function. The work will require rigorous experimental design, appropriate controls and benchmarks, statistical analysis, traceable data and predefined acceptance criteria. The role
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PhD in Political Science, Computer Science, Computational Social Science, Sociology, Economics, or a related degree. Applicants who are ABD (all but dissertation) in one of the above fields will be
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documented strong qualifications in mathematics, statistics, or mathematically founded data science, as well as excellent oral and written English language skills. Experience with spatial statistics is an
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a PhD in biology, earth system or data science, or a similar field, and have several years of experience with interdisciplinary collaborations focused on understanding biodiversity dynamics by
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Science, or a related field. No experience required. Preferred Qualifications PhD in Computational Biology, Bioinformatics, or a related field (e.g. statistics, computer science, or quantitative biology