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backgrounds in statistics, computing, machine learning (ML), and genetics and genomics, with a focus on large-scale genetic, genomic, and phenotype data. The work will involve both methodological research
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backgrounds in statistics, computing, machine learning (ML), and genetics and genomics, with a focus on large-scale genetic, genomic, and phenotype data. The work will involve both methodological research
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candidates will have access to local computing facilities and will have the opportunity to interact and work with researchers in both the Harvard College Observatory ( HCO ) and the Smithsonian Astrophysical
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learning; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic
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performance and funding. Resources for research expenses including publication charges, related conference travel, workshops, and computing resources will be available. Further information about the BHI may
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numerical astrophysics. The successful candidate will have access to our new computing facilities and will have the opportunity to interact and work with researchers in both the Harvard College Observatory
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. ● Experience working with large datasets and cloud computing environments. ● Excellent written and oral communication skills, with a track record of peer-reviewed publications commensurate with career
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and cloud computing environments. Solid background in statistical modeling and inference Excellent written and oral communication skills, with a track record of peer-reviewed publications commensurate
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developing code with a team through collaborative version control ● Experience working with large datasets and cloud computing environments. ● Solid background in statistical modeling and inference
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Bioinformatics, Systems Biology, Evolutionary Biology, Microbiology, Epidemiology, Biostatistics, Applied Math, or related field. Strong coding/computing skills. Familiarity or strong interest in the