Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
, 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
-
probabilistic modeling of weak signals in the image related to sensor noise developed on JPEG images [Taburet et al., 2020, Giboulot et al.,2021, Giboulot et al.,2022]. The modeling of the sensor noise will be
-
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
-
the Quantitative Imaging Platform of Villefranche (PIQv; https://sites.google.com/view/piqv ), which oversees the operation of the tools that the team develops. Those tools include imaging sensors, such as the
-
-electrogravimetry, a method based on QCM-type piezoelectric sensors coupled with electrochemical impedance measurements. The project targets rechargeable batteries (lithium-ion, sodium-ion, etc.) in particular, in
-
, and optimize sensor/microsensor responses. This interdisciplinary approach is essential to understand the changes of thermal/radiative properties by correlating them with the evolution of chemical and
-
complementary satellite data (e.g. multispectral, multi-frequency SAR sensors) to improve the characterization of forest structure, biomass and deforestation dynamics at various scales. Development
-
for the High-Luminosity LHC. Our primary responsibility is the integration of double-sided silicon sensors onto mechanical support structures (ladders), including the associated electrical, optical, and cooling
-
of systems in constrained environments and sensors and instrumentation. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5214-JEAGAY-082/Candidater.aspx Requirements Research
-
, robustness under varying turbulence, and autonomy for distributed systems. To address this, the group integrates Artificial Intelligence into AO control loops, using deep learning to handle sensor