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Field
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available from 01/04/2026 to contract end date of 07/03/2027. There have been many high profile achievements of Artificial Intelligence (AI) algorithms in recent years, demonstrating their effectiveness
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such an environment and using this information in control algorithms to ensure robust navigation. This project is funded by the Hauts-de-France Region. Mission The recruited person ensures research work. She will be in
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particular focus on applications relevant to the Arab world. The successful applicant will join a multidisciplinary research team working at the intersection of machine learning, algorithmic fairness, human
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research subject for this position is development of distributed processing strategies and algorithms for Large Intelligent Surfaces, including both joint baseband processing and synchronization across
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conferences and journals. Overview: The successful candidate will join an interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific
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Specific Responsibilities/activities: Maintaining long-term observational capabilities Participating in intensive field campaigns Designing novel tailor-made algorithms and inversion methods Combining
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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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satellite datasets and employ near-real-time server-side algorithms for time-series analysis and the generation of value-added geospatial maps. To enhance the sustainability performance and competitiveness
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Knowledge in modeling and algorithms for large-scale ordinary differential equations (ODEs) and differential-algebraic equations (DAEs) Proficiency in a scientific programming language (e.g., C, C++, Fortran
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central controller). ● Develop and validate calibration methods for new sensors for turbidity and existing commercial sensors. ● Explore high-frequency turbidity monitoring as a tool for assessing