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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 3 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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involve designing and implementing algorithms. Testing with actual robotic platforms is handled by Demcon. Innovative simulation data is used for both training and testing. The project is in parallel with
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algorithms. Testing with actual robotic platforms is handled by Demcon. Innovative simulation data is used for both training and testing. The project is in parallel with our dedication to teaching perception
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. We are looking for candidates whose work focuses on the broader area of Theory of Computing which includes complexity theory, algorithms, quantum computing, cryptography, differential privacy
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(https://www.ise.fraunhofer.de/en/research-projects/pvev.html ), we are working to optimize models for PV self-consumption estimation and, on that basis, to develop an algorithm for PV feed-in upscaling
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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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academic research lab environment -Software programming for scientific simulations -Interdisciplinary and multi-physics relevant strong background -Design of computational algorithms for 1st /2nd order time
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/Programming skills are essential to complete this project through geospatial modeling automation, image fusion algorithm development with advanced STARFM process, quality peer-reviewed publications, etc
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, operating systems, programming languages, formal methods, real-time systems, security and cryptography, and theory of computation and algorithms. In addition, members of the Department collaborate closely
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that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance and edge computing; The design of architectures