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imagery). Experience in building data models using Python or other statistical and/or mathematical programming packages. Proficiency in developing machine learning algorithms to analyze spatial-temporal
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training algorithms and AI architecture. Image reconstruction, segmentation, and classification. High performance computing for spatiotemporal data. Major Duties/Responsibilities: Develop foundation AI
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Description The Quantum Information team at UMass Amherst is involved with modeling and optimization of quantum hardware, as well as development of new modeling methods and algorithms, in collaboration with
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programming; Experience programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or
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algorithms, NLP models, and LLMs to analyze complex data. Designs and implements novel data science methodologies for predictive modeling, causal inference, and probabilistic analysis in clinical and
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network modeling to address real-world policy challenges through algorithm development and technical analysis. Key Responsibilities Conduct original research in generative AI Train and supervise graduate
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago
have experience with algorithms, numerical techniques, and computational methods, specifically for uncertainty quantification, Bayesian statistics, and multivariate optimization; must have excellent
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acquisition and management. This role directly builds upon Ph.D. research by applying advanced 3D imaging, algorithm design, machine learning, software engineering and visualization techniques to a real-world
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large quantities of data to gain a greater understanding of our systems and develop data analytics and artificial intelligence algorithms. You will be actively engaged in the research and development
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) invites applications for a Postdoctoral Research position focused on the trait based ecology of marine macroalgae. Community ecology is often mired in case histories explaining distribution and abundance