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Field
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can be developed using FPBTs and FBGs coupled with physically informed (PI) machine learning algorithms. SMATSH scientific objectives are then to develop computationally efficient models to predict
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algorithmic agents, the research explores how automation, predictive modeling, and generative intelligence transform sensemaking, decision-making, and adaptive capabilities. The goal is to develop a framework
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must have a research program with a strong record of peer-reviewed publication using advanced computational methods (such as computational modeling, machine learning, AI, or algorithm development
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algorithms developed for the mission. The aim of this project is to develop and test enhanced L2 algorithms for the four hydrological parameters of HydroGNSS, leveraging a combination of machine learning
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in applied research for digital security and is a pioneer in developing highly innovative concepts, algorithms and systems for anomaly detection, cyber threat intelligence, penetration testing and
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research, and training student researchers. Job Description: The Barnard Neural Computation lab headed by Dr. Gabrielle Gutierrez seeks a part-time data analyst to assist in the development of network and
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in applied research for digital security and is a pioneer in developing highly innovative concepts, algorithms and systems for anomaly detection, cyber threat intelligence, penetration testing and
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Develop new generative AI and large language models (LLMs) to support the algorithm designs for Real World Asset applications and ecosystems. Implement and test the Real World Asset framework Publish
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computational counting and sampling, including developing new algorithms and analysis techniques. Typical tools and topics are Markov chains Monte Carlo, Lovasz local lemma, high dimensional expanders, geometry
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development of algorithms and large-scale numerical simulations. Your expertise will extend to various areas, including quantum Monte Carlo, machine learning, quantum computing, quantum machine learning, and