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sensing for traffic monitoring and incident/hazard detection (including accidents, fire, and earthquake detection); design, implementation, and validation of CV algorithms to detect unsafe driver behavior
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, Boston. Specific work hours will be determined by the supervisor. What you'll do Use existing clinical and imaging data (public or within hospital) to develop AI algorithms, apply them into research and
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AI labs on scientific applications of AI (see https://www.anthropic.com/news/anthropic-partners-with-allen-institute-and-howard-hughes-medical-institute ). What you’ll do: Research and explore
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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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/algorithmic modelling. Experience with multidisciplinary IoT systems, including low‑power sensor networks and power‑constrained device architectures. Hands‑on experience with experimental testing in
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signal-to noise Post-processing: denoising, reconstruction algorithms Comparison with high-field MRI: deep-learning and other AI modalities for low-field MRI optimization Close cooperation with
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Project Scientist (Time-resolved Fluorescence Spectroscopy) - Butte Lab - Department of Neurosurgery
system parameters using standard and advanced test and measurement equipment. Develops algorithms for calibration of TRFS systems in the presence of various noise sources. QualificationsEmpty heading
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without a master’s degree have until June 30, 2026 to complete the final exam. Desired qualifications: Experience with data simulation, clustering algorithms, benchmarking, model selection and evaluation
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prediction under extreme conditions, resilient cybersecurity solutions, and self-healing algorithms. Candidates should demonstrate not only extensive experience in deep learning, reinforcement learning, and
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for the construction of the 6G telco cloud, both at the architecture and algorithmic levels. This includes scalable telco clouds, flexible functional splits, native trustworthiness, trustworthy AIML, decentralized