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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only
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Computer Vision algorithms. Experience using urban building stock modelling and urban digital twins What you will do: Design & Develop: Create data structures for detailed, spatialised construction component
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properties of macromolecules, developing novel ways to combine quantum chemical methods and machine learning, developing quantum algorithms for computational chemistry on quantum computers, and applying
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optical simulation skills (Preferably in Zemax) Strong programming skills (preferably in Python) Experience in deep learning algorithms is a plus Ability to work in a highly international team and
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that accounts for ion pairing in a consistent manner Apply the developed equation of state for different applications, including liquid-liquid equilibria, where a robust and sophisticated algorithm may need to be
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methods and tools for adaptive, AI-driven embedded algorithms to support real-time analytics and decision-making in IoT, wireless communication, and remote sensing systems Collaborate with multidisciplinary
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Schedule: Monday – Friday, 8 a.m. – 5 p.m. Summary The Sedlazeck Lab is seeking a highly motivated Staff Scientist with strong computational and algorithmic expertise to lead and support cutting-edge
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collects physiologic and biologic data in extreme environments and utilizes this data to develop novel prediction algorithms of acute mountain sickness, heat exhaustion, cold injury, and other environmental
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language models to whole genome sequencing data - Develop algorithms and neural network architectures for the prediction of structured outputs (i.e. trees, graphs) - Implement and develop methods
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measurements Calibration of materials as sensors to measure temperature, oxygen, and pH values in cells Development of models based on artificial intelligence algorithms to interpret luminescence signals Study