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Field
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absorption assessment via FDTD using EM field data Detection and localization algorithms of dynamic users using wireless signal parameters and sensor fusion Generation of Digital Twin city model that is able
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autonomy. Influences others regarding policies, practices and procedures. Essential Functions: 60% of Time the Research Scholar may: • Lead development of deep learning algorithms capable of identifying
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are met. Undertake these responsibilities in the project: Develop and deploy multimodal AI algorithms for fire, smoke, and hot-work detection by fusing optical, thermal/infrared, LiDAR, RADAR, and gas
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optical systems, force plates, EMG, etc.), as well as devices for field-based and real-life motion analysis and activity monitoring (sensors, wearables, computer vision, digital tools, etc.). Perform motion
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of intelligent assistance, including human validation in each steps where it makes sense 15 Doctorate Candidates will learn from a network of experts on network monitoring, data management, algorithms, AI
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Cluster on: Artificial Intelligence & Machine Learning Data Science Digital Twin & Simulation Cyber-physical systems AI-Driven Smart Sensor Technologies Human-computer interaction (HCI) Internet of things
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applications of various sensors and actuators; solid mathematical foundation; priority will be given to those with experience in developing industrial-grade control algorithms, signal processing algorithms
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sensor integration Automated control systems and algorithms Cyber-resilient embedded system design Testing methodologies (Hardware in the loop, software in the loop, etc.) System integration methodologies
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, and doctoral levels, the Academy's robust portfolio of 11 undergraduate programmes and 12 master's programmes coalesces around three core pillars: AI Core (Applied AI, Algorithms & Data Science
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upgrade existing imaging and sensing systems Develop new sensing techniques and technologies Calibrate sensors and ensure data integrity for use in machine learning algorithms Support R&D projects