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Field
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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
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sensor monitoring, actuator and motor control, and algorithms for signal processing, detection, classification, tracking, and decision‑making Develop and integrate autonomy‑related capabilities such as