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Field
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," focusing on the security and resilience of critical cyber-physical systems (CPS). The core objective is to develop advanced contextual-based intrusion and anomaly detection algorithms to mitigate insider
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equipment, electronics and sensor integration, and working effectively within multidisciplinary teams. Experience with mobile manipulator robots, Linux, ROS/ROS2, algorithm design, and laboratory automation
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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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algorithms that can tightly integrate physical hardware, sensors, and computational models. This PhD position is centered on addressing these challenges through innovative computational methods, combining
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, where fibre optic sensors are being developed to measure strain in real time during the cure process. As part of this exciting project, we are collaborating with our spin-out company specializing in
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saccades oculaire, scores EVA/Borg >6) ; (iii) Validation de l'efficacité de l'adaptation par algorithme des séances d'activités physique via une étude randomisée contrôlée sur 6 séances avec évaluation de
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research institutions and companies from 8 European countries. The research will focus on developing contextual-based intrusion and anomaly detection algorithms to cope with the presence of insider attackers
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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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(GPR), complemented by soil sensors and borehole data. A particular emphasis will be placed on the combined use of borehole and surface GPR, as well as small-scale EMI measurements in controlled
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of technological innovation aimed at sup- porting farmers by alerting them of imminent lambing events. Its objective is to provide a reliable predictive system based on sensors and artificial