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Field
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
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PhD with an interest in computational social science or data science (broadly defined). This individual will help design, execute, and analyze a series of field experiments on a platform created by the
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in core methods of machine learning/artificial intelligence. ● Experience with data warehousing and building large, curated datasets with protected health information, suitable for training large
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, and contributing to a robust data infrastructure that makes large-scale, multimodal datasets FAIR and AI-ready. You will publish findings in high-impact journals, present at major international
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the Rosaceae, Citrus, Vaccinium and Pulse crop databases to meet community demand for resources with usable big data aggregated, analyzed, integrated, and visualized, as well as data management and analysis
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-performance computing systems (,e.g., SLURM) Familiarity with population genomics, GWAS, or related approaches Experience with large biological datasets Proven ability to communicate scientific information
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Science Lab is seeking a highly-motivated postdoctoral scholar to lead a large-scale, community-based, clinical trial of behavioral treatment for childhood obesity. The Solutions Science Lab designs
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the ability and motivation to develop expertise in large-scale model training and scaling on HPC systems, as well as in handling the unique characteristics of scientific data, including large-scale numerical
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agents for X-ray spectroscopy by integrating large language models (LLMs) with physics-aware spectroscopy workflows. The researcher will work closely with a multidisciplinary team of X-ray physicists and