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-computer interaction. The ideal candidate will have a strong foundation in algorithms, HCI, and software engineering, and will contribute to the design, development, and evaluation of interactive and
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Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job A Postdoctoral Research Fellow position is available to work on research related to development of predictive display systems
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by working on research projects in AI safety and robotics, contributing across the full research lifecycle: algorithm and system design, code development, experimental validation, and scientific
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settings. Develop and test algorithms for object detection, tracking, and classification using wireless sensors. Help guide and mentor graduate students and other junior team members working on the project
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. Responsibilities* Design, implement, and evaluate wireless-based experiments in lab and real-world settings. Develop and test algorithms for object detection, tracking, and classification using wireless sensors
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 3 hours 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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unsupervised techniques, time-series modeling, and clustering algorithms. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple
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scientific software development. Knowledge of algorithms and data structures to produce efficient, maintainable, well-documented code. Skilled in data handling, cleaning, and preprocessing; experience managing
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of computer vision algorithms, with a focus on assessing performance in edge deployment settings. Their role will involve supporting model development, running experiments, and analyzing results to evaluate
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of Computation Group, seeks applicants for a postdoctoral fellowship to conduct research in differentially private learning, its connections to replicability of algorithms, and algorithmic fairness. Basic