406 machine-learning-"https:" "https:" "https:" Postdoctoral positions in United States
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to collaborate with other researchers, and mentor graduate and undergraduate students, and may also teach one course per year for the Department of Statistics. A Ph.D. in Statistics, Biostatistics, Machine
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modeling and risk assessment, and demonstrated experience in one or several of the following areas: Tropical cyclone dynamics and thermodynamics Statistical and/or machine learning approaches to weather
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, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https://resources.uta.edu/hr/services/records/compensation
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analysis Large language models or machine learning/predictive modeling for longitudinal data analysis Strong computer programming skills Strong mathematical or statistical skills Ability to work as a part of
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research on how engineers learn, how engineering knowledge and identities are formed, how we assess and evaluate learning, and how educational systems can be designed and transformed to support meaningful
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field Computer Skills General office suite and willingness to learn lab specific programs Strong programming skills in R and/or Python Experience with Next Generation sequencing Record of peer-reviewed
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) Cleaning and managing large datasets from administrative data sources or online learning platforms Causal machine learning (e.g., double/debiased machine learning (DML), causal forests, generic ML) Learning
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to contribute to one or more projects, learning advanced cellular and molecular biology and anaerobic microbiology techniques. The candidate’s day will be split between benchwork to generate data, and computer
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multi-omic sequencing, network biology, and machine learning to identify actionable biomarkers and therapeutic vulnerabilities. The successful candidate will work at the interface of computational
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applications, and ability to quickly learn and master various computer programs. Must be technically rigorous, organized, and have demonstrated excellence, innovation, and productivity in research. Ability