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-house equipment. Apply your technical expertise across areas such as PCB design, embedded systems, sensors, actuators and data acquisition. Work collaboratively with engineers, academics, students
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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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, finite difference schemes, stability analysis (Von Neumann), algorithms forN-fields (Barnes–Hut) and numerical integration. Theoretical foundations of general physics and astrophysics. The principles
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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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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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within a Research Infrastructure? No Offer Description The research program aims to develop models, algorithms, and architectures for implementing Digital Twin-enabled Smart Ecosystems, with a particular
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devices and components including valve dampers, receiver-controllers, relays, control panels, sensors, transmitters, thermostats, meters and other similar components and devices. 3. Tests and analyzes
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devices and components including valve dampers, receiver-controllers, relays, control panels, sensors, transmitters, thermostats, meters and other similar components and devices. 3. Tests and analyzes
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data acquired by mobile road monitoring systems using heterogeneous sensors, such as images, laser profilometers, GPS, and audio data, will integrate machine learning, computer vision, and image analysis
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with Photonic/Optical sensors include spectrometer and/or LiDAR • Familiar with programming and data visualization • Familiar with AI, machine learning • Creative and