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Job Title Postdoctoral Research Associate Agency Texas A&M University Department Teaching, Learning And Culture (Tlac) Proposed Minimum Salary Commensurate Job Location College Station, Texas Job
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partner who focuses on the underlying machine learning models, and you will help coordinate the joint work across the two teams. The position offers a strong publication trajectory at leading HCI venues
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—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive
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License/Registration/Certification n/a Physical Requirements Some standing or walking. Sitting at computer workstation for extended periods. Repetitive motion. Lifting, pushing, or pulling of objects up
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, sex, age, national origin, disability, marital status, sexual orientation, military/veteran status, gender identity, or other non-merit factors. We welcome and encourage applications from minorities
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. • Possess molecular virology or protein biology skills. • Should be enthusiastic and dedicated, have the ability and interest in learning new techniques; must be able to follow verbal and written instructions
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of natural language processing, machine learning, artificial intelligence, and human-computer interaction. Established within the School of Computer Science, LTI pioneers innovative approaches to understanding
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computational and machine learning approaches. What We Offer As an employer, we genuinely care about our employees’ wellbeing and this is reflected in the range of benefits that we offer including
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, Texas A&M AgriLife is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about how you can be a part of
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 11 days ago
include advancing foliar trait retrieval algorithms, atmospheric correction over dense humid tropical canopies, machine learning and dimensionality reduction for handling big data, landscape evolution