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Field
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to the development of innovative solutions and algorithms to enhance 5G network capabilities. Work closely with the Principal Investigator (PI), Co-PI, faculty and research team to plan, track, and deliver project
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are developing a groundbreaking technique based on autofluorescence (AF) imaging and Raman spectroscopy for detection of positive lymph nodes during breast cancer surgery. The instrument will allow surgeons
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Cancer Centre based in the Biodiscovery Institute. We are developing a groundbreaking technique based on autofluorescence (AF) imaging and Raman spectroscopy for detection of positive lymph nodes during
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hyperspectral imagery, useful for disease detection and monitoring. Develop validated satellite data simulation methodology. Develop disease detection and differentiation algorithms demonstrably effective on both
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for the position of Research Fellow for the project Anticipatory Robotics. Project Introduction: The project titled Anticipatory Robotics aims to improve Human-Robot Interaction by developing a “Robot Body Language
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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | about 1 month ago
multidisciplinary team involving image processing labs (e.g., CMAP) and French hospitals (e.g., CHU Kremlin-Bicêtre, Hôpital Foch, Institut Mutualiste Montsouris, Institut Gustave Roussy). Phase 1: Design and develop
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Communications Group: Optical Communication Systems and Networking – AV Work Objectives: 1) Development and implementation of novel digital signal processing (DSP) algorithms, adaptive forward error correction
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. The project seeks to develop mathematical models for allocation of critical resources during pandemics. Populating these instances with real-world data we would then develop novel algorithms to solve them
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opportunities exist at the intersection of mathematics, computer science, statistics, and their scientific applications, with the lines between theory, algorithm development and software implementation often
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions