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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 2 months 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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testing of model-free algorithms for real-time optimization of turbine operating conditions (e.g., yaw set points). Other projects may be assigned by the supervisor depending on skills and technical needs
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evaluation of algorithms for early drought stress detection by integrating interactive manipulation strategies with learning-based monitoring methods. This includes designing interaction primitives, processing
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predictive analytics Human factors, behavior science, and patient-centered design Advanced computing and scalable algorithms Decision science and learning health systems design Qualifications Required: Ph.D
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, telecommunications or related field. Other requirements include Strong background in communication theory, signal processing, and wireless communications, Extensive experience in physical (PHY) layer algorithm design
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to integrate large and complex preference datasets with information at individual level, with specific attention to open and reproducible research, e.g., in the development of codes and algorithms. We will focus
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sequencing instruments into microscopes! Your mission You will join our team, a leading group in the field of DNA nanotechnology specialized in creating molecular tools for life-science research (http
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computing and scalable algorithms • Decision science and learning health systems design Qualifications Required: • Ph.D. in Systems Engineering, Industrial Engineering, Operations Research, Computer Science
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their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? You will be part of LIST’s Remote sensing and natural resources modelling group Embedded in
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) • Contributing to analyses of agency, responsibility, trust, mental privacy, and algorithmic bias in neuroAI systems • Collaborating with technical partners on issues of transparency, interpretability, and