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Field
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linguistics, semantics, syntax, pragmatics, computational linguistics, phonology, or sociolinguistics. We would like to see candidates who can communicate with and learn from both linguists and scholars in
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learn best practices in instructional design and health communication, including web communication, application of CDC’s quality training standards, user experience, digital content design, and plain
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crop area and learn basic agronomic, data collection, and plant breeding methodologies in trials and nurseries planted at the USDA-ARS. Learning Objectives: The project assignments will provide you with
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a machine learning model for storm surge prediction. Key Responsibilities Participate in and manage the research project with Principal Investigator (PI), Co-PI and the research team members to ensure
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Center for Biologics Evaluation and Research (CBER) | Southern Md Facility, Maryland | United States | 1 day ago
are held in zoos, research facilities, venom producing facilities, and by private individuals. Exotic antivenoms are also acquired by US military for treatment of personnel positioned in other nations
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verbal, written communication skills In-depth knowledge of deep learning, specifically VLM models, computer vision techniques (e.g., open vocabulary object detection, model distillation, VQA, test-time
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 1 day ago
artificial intelligence (AI) and statistical programming skills in R and/or SAS by implementing end-to-end machine learning pipelines, including data preprocessing, model training, cross-validation, simulation
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/ESA_selects_Harmony_as_tenth_Earth_Explorer_mission ) The candidate will develop and apply cutting-edge remote sensing or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via
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multivariate, statistical-genetic or machine-learning approaches. · Research involving developmental, ageing or neuropsychiatric cohorts, including longitudinal or large-scale population datasets. · High
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, chromatin profiling, genomics, spatial transcriptomics and single-cell data. Apply statistical, machine learning, and network-based approaches to analyze high-dimensional biological data. Collaborate closely