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. Who are you? We are seeking a talented and motivated candidate to implement Artificial Intelligence/Machine Learning (AI/ML) algorithms for modeling and improving the observation of hydrological
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interventions, and facilitate estimating the effectiveness of these interventions. Where to apply Website https://resurseumane.ase.ro/ai-for-energy-finance-ai4efin-760048-23-05-2023-cer… Requirements Research
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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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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 12 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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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
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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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Operations Manager to lead and monitor operations for continuous algorithm improvement and validation. This will involve guiding the development of data management systems and establishing data assessment
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-board Payload Signal and Data Processing algorithms and techniques for RF payloads and instruments in close collaboration with TEC-ED; and Time and frequency references, modelling, design tools
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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