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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months ago
Lidar and the Roscoe upper troposphere/lower stratosphere lidar). Additional projects include the development of machine learning and advanced data processing algorithms, and participation in upcoming
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25 Jan 2026 Job Information Organisation/Company CNRS Department Institut de Recherche en Informatique Fondamentale Research Field Computer science Mathematics » Algorithms Researcher Profile First
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months ago
retrieval algorithm development with focus on using the polarimetric signals, the new FIR or sub-mm bands, and/or the ML/AI approach; (3) ML/AI application on system/pattern tracking on satellite images
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quality of life, support preventative medicine, and reduce healthcare burdens. Specific objectives include the development of wearable ultrasound imaging systems for doing cardiovascular echography, AI
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electron beams, advanced beam-manipulation for precise electron-beam shaping, and ML for accelerator science. Responsibilities Develop and deploy ML algorithms for autonomous operations and optimization
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Hub for Earth Observation and Geospatial Information by offering knowledge, responsible research, open innovation and application services capable of sustaining Cyprus’ development. ERATOSTHENES Centre
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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spearhead technical development, data analysis, and publication efforts for a student-led initiative using OpenBCI hardware, EEG recordings, eye-tracking integration, and machine learning to enhance neural
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entities, such as robots, vehicles, or sensors, forms internal representations of space, time, and motion when interacting in complex non-stationary environments. The objective is to study and develop models
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disease progression. This includes integrating LLMs with structured data sources to develop robust computational phenotyping algorithms and scalable models for real-world evidence generation. The role will