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Field
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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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devices—such as wearable sensors, assistive robotics, or implantable systems—where real-time performance, energy efficiency, and reliability are critical. Unlike traditional NAS approaches that are hardware
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RF information and limited communication. A key contribution of the project is the design of frugal embedded control algorithms tailored to resource-constrained robotic platforms. These control
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of autonomous monitoring units, including image sensors, edge computing units, batteries, solar panels, and LoRa/LTE communication modules. • Implementation of anonymous visitor detection and counting algorithms
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: The Research Scholar will support research on a smart toilet system for automated urine output monitoring through clinical study management, quantitative analysis of clinical and sensor-derived data, algorithm
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graduate to work full time on a 27-month Knowledge Transfer Partnership (KTP) to develop an advanced aftermarket sensor-led condition and monitoring solution upgrade for the EDM engineering processes
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analogue field tests; mathematical modelling and simulations; research and integration of novel sensors for robot perception; algorithmic design and computer programming implementation of building blocks
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and implementing computer vision and perception pipelines for challenging real-world, multimodal sensor environments, supported by strong expertise in ML, DL and AI algorithms. Proficient in training
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scheduling algorithms to ensure operation continuity and reliable information exchange among batteryless sensors. Where to apply Website https://lavoraconnoi.unitn.it/research-assignments/dipartimento-disi