11 systems-science "https:" "https:" "https:" "https:" "RTU Riga Technical University" positions at CEA
Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
(or equivalent) in signal processing, applied physics, electrical/electronic engineering, or data science with an interest in physical systems. •Strong motivation for interdisciplinary research, solid foundation
-
foundations in waveform design and advanced signal processing. In particular, energy-efficient precoding algorithms have been developed for distributed MIMO systems and are currently validated through numerical
-
be part of the IBS/IRIG Institute! The position is available to investigate the enzymatic chemistry orchestrated in the microbial anaerobic carbon cycling. The offer is part of the ERC Project
-
Engineering (PHELIQS, https://www.pheliqs.fr/en ), a joint research unit of the French Commission for Atomic Energies and Alternative Energies (CEA), Université Grenoble Alpes and Grenoble INP, is currently
-
material fracture is paramount in the nuclear industry to ensure the structural integrity and long-term safety of critical components. Therefore, accurately simulating the onset of cracks and their
-
Category Chemistry Contract Internship Job title Stage BAC+5 Internship in Materials Chemistry Engineering for a master's degree H/F Subject Electrochemical characterization of Hybrid Solid Polymer
-
. Qualifications Ph.D. in Physics, Materials Science, or a related field. Hands-on experience with cleanroom nanofabrication. Familiarity with transport experiments (thermal, electrical) is a plus. Strong analytical
-
, with a possible extension, and be part of the IBS/IRIG Institute! The position is available to investigate the enzymatic chemistry orchestrated in the microbial anaerobic carbon cycling. The offer is
-
for spintronic systems. • Contribute to the preparation and coordination of national and international collaborative projects, including competitive funding applications. • Develop and strengthen scientific
-
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