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of creating robust radio frequency fingerprinting (RFF) systems using machine learning. The successful candidate will have significant experience in signal processing or machine learning, and an outstanding
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 18 hours ago
Posting Description SENIOR ADMINISTRATIVE ASSISTANT, HSI, Sloan School of Management, serves as primary point of contact for HSI activities and communications; responds to inquiries requiring an in
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of hardware description languages. Experience in efficient implementation of signal processing or artificial intelligence algorithms. Experience in performance characterisation and energy consumption analysis
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Description The goal of this project is to explore the effects of mechanical forces on the cell nucleus during epithelial-mesenchymal transition (EMT), a critical cellular process that occurs in both
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and cellular decision-making processes. Intrinsically disordered regions are increasingly recognized as dynamic regulatory platforms that mediate signaling specificity, environmental responsiveness, and
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receptionist, this position serves as a helpful first point of contact and plays a key role in creating a positive experience for those interacting with the University. The position also supports essential HR
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mass spectrometry data, RNA sequencing, or imaging. The candidate should also have a background in working with hierarchical data and resolving ambiguous, overlapping signals. Strong programming skills
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recruitment is part of the CHRONOS maturation project supported by SATT LUCTECH. Mission Your main mission will be to contribute to the software design of an innovative HD-sEMG signal measurement and processing
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performance in wired/wireless communications and advanced sensing, is pushing the integration of analog/RF/mm-wave and mixed-signal systems. In these domains, energy-efficient wideband signal processing and
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are still present. This PhD research aims to develop new and efficient AI-based solutions for processing LiDAR data (2D/raster and 3D/point clouds) and improve the detection of sub-canopy archaeological