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Field
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. Understanding of object-oriented programming, data structures, algorithms, and software design practices. Development experience with Matlab, Python and C/C++ programming languages. ML/LLM models knowledge and
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of many teaching and research awards, with strengths in the Department including algorithms, artificial intelligence and machine learning, data science, bioinformatics, computational neuroscience
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 15 days ago
distribution on a global scale. This project will focus on developing a strategy to best utilize this data in a global atmospheric data assimilation framework. Activities that would be involved in this project
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4 Jul 2026 Job Information Organisation/Company CNRS Department Institut de recherche en informatique et systèmes aléatoires Research Field Computer science Mathematics » Algorithms Researcher
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. Understanding of object-oriented programming, data structures, algorithms, and software design practices. Development experience with Matlab, Python and C/C++ programming languages. ML/LLM models knowledge and
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and inference for distributed AI agents, and how the realities of networked operation shape the design of adaptive, resilient intelligence. Finally, the research will address how groups of embodied
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and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems. The department has extensive international collaboration with
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, development and implementation of digital signal processing algorithms and data processing strategies for microwave radiometers; conducting laboratory activities to validate architectural concepts, operating
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coordination and shared situational awareness, fostering the human-agent teaming essential for future mission success. By bridging the gap between sophisticated algorithms and real-world application, our team
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evaluation into the algorithm spreadsheet and Pipeline Report and miscellaneous ad hoc reports for periodic review by faculty Integrate Admissions with other teams Trains and supports faculty and staff