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) modules into safety-critical embedded systems (autonomous vehicles, drones, industrial and medical devices) raises major safety and security concerns. These modules, often based on deep neural networks
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. These modules, often based on deep neural networks, are sensitive to both accidental faults and intentional attacks [1]-[5] that can alter their decisions. Ensuring their robustness under real-world conditions is
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(LMFL). Opportunity to work in an interdisciplinary environment combining micro-sensor technology, aerodynamics, and instrumentation. Networking: Integration into the FR TTM CNRS research federation and
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Technologies Initiative (https://wirelesspower.ieee.org/ ). This project is characterized by a strong multidisciplinarity, with applications in the field of IoT. You will work on electronics, wireless
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, photonic devices for communications, optical networks, and fiber sensors. SCIENTIFIC CONTEXT Optical fiber forms the backbone of the communication systems. The exponential increase in data traffic is putting
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; data collection and analysis. The various tasks must be able to be performed independently: Stereotaxic surgery in mice for AAV-based sensors injection and implantation of intracerebral recording devices
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-type-sensors, standalone net radiometer, radiometer combined with black globe, and six global radiative fluxmeters, among others; The formulation of a straightforward technique for assessing the thermal
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the advent of the Internet of Things (IoT), any sensor can be interfaced with a local network or the Internet. This massive deployment has created many security issues and associated solutions
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datasets generated by our collaborators; ensure timely delivery to meet network/dynamic modeling needs. 1. Tissue & cellular phenotyping of intestinal ageing across life stages in vertebrate models
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- Rad) provide continuous but coarse radiative fields (5–30 km). High-resolution sensors such as Sentinel-2, Landsat, ECOSTRESS, and SDGSAT-1 offer fine spatial detail but sparse temporal coverage and