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Field
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with computational scientists to integrate and interpret large-scale genomics data. Present research findings in lab meetings, departmental seminars, and conferences; prepare manuscripts and fellowships
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 15 hours ago
combination of observational data, machine learning techniques, and cosmological simulations. The group is actively involved in multiple JWST Guaranteed Time Observation (GTO) and General Observer (GO) programs
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Leinweber Institute for Theoretical Physics (LITP). Potential research topics include: inflationary cosmology and its connection to late-time observables; large-scale structure theory and analysis; cosmic
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at the field, drone, and satellite scale. Big data analysis including processing and analyzing EMIT hyperspectral datacubes over large spatial domains such as the Colorado Plateau and even global. Presenting
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for longitudinal data analysis or large language models. Strong motivation in the development and application of digital twins, particularly in health and biomedical contexts. Strong computer programming skills
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. Analyzes large data sets. Analyzes next generation sequencing data (e.g., RNS-seq, whole genome/exome sequencing). Uses state-of-the-art bioinformatical and statistical tools. Develops new statistical
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Pharmacology, the Clinical and Translational Science Institute and the McKnight Brain Institute, all of which provide state-of-the-art environments and large data sets access. Expected Salary: $62,232 annually
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several areas of ongoing research in the laboratory, including the isolation and genomic analysis of large DNA viruses, cultivation of diverse hosts, analysis of evolutionary relationships and genomic
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, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific literature). Our
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-based models, and remote sensing technologies. Required Qualifications • Ph.D. in Civil or Environmental Engineering, Hydrology, Data Science, Geosciences, Computer Science, or a related field. PhD must