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Field
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of the following backgrounds are particularly encouraged to apply: Machine learning and deep learning, particularly for time-series modeling Structural health monitoring (SHM), including fiber-optic sensor
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repeats to test how these contribute to functional specificity of FLK. Test binding of FLK and/or FLK variants to transcripts of candidate target genes. Identify FLK targets through ultra-deep RNA-seq and
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preparing progress reports. Additional duties as assigned. Knowledge, Skills and Abilities: Knowledge of deep learning, machine learning, image processing, and EO/IR and RF signal processing. Strong analysis
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theory and applications, instrumentation and signal processing and cloud microphysics and dynamics. Strong programming abilities (like Python), statistical knowledge, machine learning expertise, and data
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States of America [map ] Appl Deadline: (posted 2025/01/10, listed until 2025/07/10) Position Description: Position Description Multiphysics, Machine Learning, and Uncertainty Quantification Postdoctoral Positions Los
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Deep Learning-type methods. The focus will be on geodesic methods, the search for paths of minimum length according to an adapted metric, imposing for example a penalization of the curvature. In addition
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cutting edge technologies in robotics and AI (Autonomous Robots, SLAM, Navigation, Perception, Human-Brain Interface, Deep Learning and their Security, etc.). CAIR has state of the art autonomous unmanned
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have deep experience in wireless communications, deep learning, signal processing, networked systems, and/or software-defined radios such as USRP. The candidates should have a Doctoral Degree in EE/CS
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at least one year of postdoctoral research, have experience with data visualisation, and be enthusiastic about engaging with different online communities to learn more about public uses of the past. They
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tomography research team with, for this position, a particular emphasis on computational imaging and deep learning. Tomography is an image reconstruction technique that leans strongly on large-scale numerical