10 embedded-systems-"https:"-"https:"-"https:"-"https:"-"https:" positions at CEA in France
Sort by
Refine Your Search
-
Category
-
Field
-
their protection against physical attacks a major challenge. Among these threats, clock glitch attacks remain a powerful and accessible fault injection technique, especially relevant for IoT and embedded systems due
-
, electronics or embedded systems. Required skills/knowledge: -HW description languages: SystemC, VHDL, Verilog, SystemVerilog, CHISEL - Knowledge of digital electronic architecture (processors, Caches, NoC
-
are looking for a creative and enthusiastic researcher with a strong background in one or more of the following areas: · Thermodynamics and energy systems; · Multiphase flows and fluid mechanics; · CFD
-
project is to achieve the integration of quantum devices within Fully Depleted Silicon-On-Insulator (FD-SOI) technology on a 300 mm platform. The success of this integration critically depends
-
(en mois) 6 Description de l'offre Missions : Graphite is the reference negative-electrode material in Li-ion batteries, but its lithiation involves complex phase transitions and staging. Quantitative
-
ESTHER is a 1D Lagrangian code for radiation-matter interaction that studies the evolution of materials transitioning from the solid phase to the plasma phase under the effect of an intense radiation pulse
-
on this benchmark. - Write a publication about this benchmark. #Cea List Moyens / Méthodes / Logiciels AI, Deep Neural Network, Computer Vision, Human behavior analysis Profil du candidat Profile - Students in
-
of possible atomic configurations exceeds 2^2500? That is the bottleneck our IRESNE (nuclear fuel physics) and LIST (AI) teams have just cracked with PULSE, a generative (VAE) method published in Nature
-
1,000 seconds. This postdoctoral position is part of a three-year collaborative project led by the Plasma Physics Laboratory (LPP) at École Polytechnique and involving German partners. The project aims
-
The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented