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Field
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completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
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algorithms for critical equipment (e.g., compressors, reaction kettles). Research advanced process control strategies and deploy them on the center's distributed control systems and edge computing devices
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of Oxford. The post is funded by United Kingdom Research and Innovation (UKRI) and is for 24 months. The researcher will develop 3D mapping and reconstruction algorithms with relevance to mobile robotics
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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 5 hours ago
@orau.org Qualifications Background in engineering, computer science, or a similar field. Background in software tools such as Matlab, Python, and C/C++ Experience developing machine learning algorithms
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AI Algorithm and Autonomous Decision-Making Systems RESPONSIBILITIES Research and develop autonomous decision-making algorithms for process industries (e.g., chemical, pharmaceutical, materials
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Requirements REQUIRED: Ph.D. in computer science, mechanical engineering, applied math, or related field with significant research experience in machine-learning/AI algorithm development, specifically in deep
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The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL). Northwestern, TTIC, and UIC. Position ID: Northwestern-The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL
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: Computational Sciences / Bio-computing Appl Deadline: (posted 2025/11/10, listed until 2025/11/30) Position Description: Apply Position Description As a computing researcher, you will be primarily responsible
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on developing and analyzing algorithms, building reproducible computational studies, and disseminating results in leading venues. The position collaborates with faculty, students, and external partners
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI