Lightweight CNN and Causal GNN for scene understanding
Scene understanding is a major challenge in computer vision, with recent approaches dominated by transformers (ViT, LLM, MLLM), which offer high performance but at a significant computational cost. This thesis proposes an innovative alternative combining lightweight convolutional neural networks (Lightweight CNN) and causal graph neural networks (Causal GNN) for efficient spatio-temporal analysis while optimizing computational resources. Lightweight CNNs enable high-performance extraction of visual features, while causal GNNs model dynamic relationships between objects in a scene graph, addressing challenges in object detection and relationship prediction in complex environments. Unlike current transformer-based models, this approach aims to reduce computational complexity while maintaining competitive accuracy, with potential applications in embedded vision and real-time systems.
Development of automatic gamma spectrum analysis using a hybrid machine learning algorithm for the radiological characterization of nuclear facilities decommissioning.
The application of gamma spectrometry to radiological characterization in nuclear facility decommissioning, requires the development of specific algorithms for automatic gamma spectrum analysis. In particular, the classification of concrete waste according to its level of contamination, is a crucial issue for controlling decommissioning costs.
Within CEA/List, LNHB, in collaboration with CEA/DEDIP, has been involved for several years in the development of tools for the automatic analysis of low-statistics gamma spectra, which can be applied to scintillator detectors (NaI(Tl), plastics). In this context, an original approach based on a hybrid machine learning/statistics spectral unmixing algorithm has been developed for the identification and quantification of radionuclides in the presence of significant deformations in the measured spectrum, due in particular to interactions between the gamma emission from the radioactive source and its environment.
The proposed subject follows on from thesis work that led to the development of the hybrid algorithm with the aim of extending this approach to the radiological characterization of concrete surfaces. The candidate will be involved in the evolution of the hybrid machine learning/statistical algorithm for the characterization of concrete for classification as conventional waste. The work will include a feasibility study of modeling the deviations of the learned model to optimize the robustness of decision-making.
Optimized control of a modular energy hub with minimal EMC signature
The integration of renewable energy sources (RES) has become an important issue for power converters. The increasing number of these converters and their average utilization rate allows for a rethink of energy exchange management at the system level. This leads us to the concept of an energy hub, which can interface, for example, a photovoltaic (PV) system, an electric vehicle, a grid, and stationary storage with loads.
The main objective of this thesis is to improve the efficiency, compactness, and modularity of the energy hub through control. Several ideas emerge to achieve this, such as advanced control to minimize losses, the use of AC input opposition to reduce electromagnetic compatibility (EMC) filtering, series/parallel DC output configurations to address 400Vdc/800Vdc batteries, and increasing the switching frequency to reduce volume, etc.
Thus, this thesis will, in the medium term, lead to the development of an optimal converter in terms of both energy efficiency and environmental impact.
Learning Mechanisms for Detecting Abnormal Behaviors in Embedded Systems
Embedded systems are increasingly used in critical infrastructures (e.g., energy production networks) and are therefore prime targets for malicious actors. The use of intrusion detection systems (IDS) that dynamically analyze the system's state is becoming necessary to detect an attack before its impacts become harmful.
The IDS that interest us are based on machine learning anomaly detection methods and allow learning the normal behavior of a system and raising an alert at the slightest deviation. However, the learning of normal behavior by the model is done only once beforehand on a static dataset, even though the embedded systems considered can evolve over time with updates affecting their nominal behavior or the addition of new behaviors deemed legitimate.
The subject of this thesis therefore focuses on studying re-learning mechanisms for anomaly detection models to update the model's knowledge of normal behavior without losing information about its prior knowledge. Other learning paradigms, such as reinforcement learning or federated learning, may also be studied to improve the performance of IDS and enable learning from the behavior of multiple systems.
Side-Channel based Reverse-Engineering
The characterization of the security of embedded systems in "black box" or "gray box" against Side-Channel attacks often requires a preparatory phase of Reverse-Engineering, which can be particularly time-consuming, especially on a complex System-on-Chip that can be found in smartphones or in the automotive industry. This phase can, for example, consist of detecting a cryptographic primitive within Side-Channel measurements for a future observation attack, or a target routine for a fault injection attack. The objective of this thesis is to develop a methodology and non-profiled tools that allow the automation of this detection phase, while enabling the exploitation of prior knowledge of a potential attacker.
Electromagnetic Signature Modeling and AI for Radar Object Recognition
This PhD thesis offers a unique opportunity to work at the crossroads of electromagnetics, numerical simulations, and artificial intelligence, contributing to the development of next-generation intelligent sensing and recognition systems. The intern will join the Antenna & Propagation Laboratory at CEA-LETI, Grenoble (France), a world-class research environment equipped with state-of-the-art tools for propagation channel characterization and modelling. A collaboration with the University of Bologna (Italy) is planned during the PhD.
This PhD thesis aims to develop advanced electromagnetic models of near-field radar backscattering, tailored to radar and Joint Communication and Sensing (JCAS) systems operating at mmWave and THz frequencies. The research will focus on the physics-based modeling of the radar signatures of extended objects, accounting for near-field effects, multistatic and multi-antenna configurations, as well as the influence of target materials and orientations. These models will be validated through electromagnetic simulations and dedicated measurement campaigns, and subsequently integrated into scene-level and multipath propagation simulation tools based on ray tracing. The resulting radar signatures will be exploited to train artificial intelligence algorithms for object recognition, material property inference, and radar imaging. In parallel, physics-assisted AI approaches will be investigated to accelerate electromagnetic simulations and reduce their computational complexity. The final objective of the thesis is to integrate radar backscattering-based information into a 3D Semantic Radio SLAM framework, in order to improve localization, mapping, and environmental understanding in complex or partially obstructed scenarios.
We are seeking a student at engineering school or Master’s level (MSc/M2), with a strong background in signal processing, electromagnetics, radar, or telecommunications. An interest in artificial intelligence, physics-based modeling, and numerical simulation is expected. Programming skills in Matlab and/or Python are appreciated, as well as the ability to work at the interface between theoretical models, simulations, and experimental validation. Scientific curiosity, autonomy, and strong motivation for research are essential.The application must include a CV, academic transcripts, and a motivation letter.
Study of Failure Modes and Mechanisms in RF Switches Based on Phase-Change Materials
Switches based on phase change materials (PCM) demonstrate excellent RF performance (FOM <10fs) and can be co-integrated into the BEOL of CMOS processes. However, their reliability is still very little studied today. Failure modes such as heater breakage, segregation, or the appearance of cavities in the material are shown during endurance tests, but the mechanisms of these failures are not discussed. The objective of this thesis will therefore be to study the failure modes and mechanisms for different operating conditions (endurance, hold, power). The analysis will be carried out through electrical and physical characterizations and accelerated aging methods will be implemented.
Enhanced Quantum-Radiofrequency Sensor
Through the Carnot SpectroRF exploratory project, CEA Leti is involved in radio-frequency sensor systems based on atomic optical spectroscopy. The idea behind the development is that these systems offer exceptional detection performance. These include high sensitivity´ (~nV.cm-1.Hz-0.5), very wide bandwidths (MHz- THz), wavelength-independent size (~cm) and no coupling with the environment. These advantages surpass the capabilities of conventional antenna-based receivers for RF signal detection.
The aim of this thesis is to investigate a hybrid approach to the reception of radio-frequency signals, combining atomic spectroscopy measurement based on Rydberg atoms with the design of a close environment based on metal and/or charged material for shaping and local amplification of the field, whether through the use of resonant or non-resonant structures, or focusing structures.
In this work, the main scientific question is to determine the opportunities and limits of this type of approach, by analytically formulating the field limits that can be imposed on Rydberg atoms, whether in absolute value, frequency or space, for a given structure. The analytical approach will be complemented by EM simulations to design and model the structure associated with the optical atomic spectroscopy bench. Final characterization will be based on measurements in a controlled electromagnetic environment (anechoic chamber).
The results obtained will enable a model-measurement comparison to be made. Analytical modelling and the resulting theoretical limits will give rise to publications on subjects that have not yet been investigated in the state of the art. The structures developed as part of this thesis may be the subject of patents directly exploitable by CEA.
Superconducting Silicon and detection in the far Infrared Universe
Silicon technologies occupy a central position in today’s digital landscape, both for the fabrication of semiconductor devices and for the development of advanced sensors. In 2006, the discovery of superconductivity in silicon heavily doped with boron opened a new field of research. Since then, several laboratories, including CEA, have been investigating its electronic properties and potential applications. This emerging material exhibits particularly attractive characteristics for systems operating at sub-Kelvin cryogenic temperatures, especially in the fields of quantum electronics and ultra-sensitive detectors used in fundamental physics and astrophysics.
Despite these advances, the understanding of superconducting silicon remains incomplete, particularly regarding its thermal, mechanical, and optical properties at the micrometric scale. The proposed PhD aims to address these gaps by combining modelling, design, technological fabrication, and cryogenic characterization of prototype devices, within a close collaboration between CEA-Léti and CEA-Irfu. The main objective will be to develop a new generation of detectors based on this superconducting material and to demonstrate their relevance for the detection of electromagnetic radiation in the terahertz and far-infrared ranges.
Next-Gen Surface Analysis for Ultrathin Functional Materials
Advanced nanoelectronics and quantum devices rely on ultrathin oxides and engineered interfaces whose chemical composition, stoichiometry and thickness must be controlled with sub-nanometer precision. LETI is installing the first 300-mm multi-energy XPS–HAXPES tool with angle-resolved capability, enabling quasi in situ chemical metrology from deposition to characterization.
This PhD will develop quantitative, multi-energy and angle-resolved XPS/HAXPES methodologies for ultrathin oxides and oxynitrides, validate measurement accuracy, and establish robust protocols for quasi in situ transfer of sensitive layers. Applications include advanced CMOS stacks and quantum Josephson junctions, where sub-2 nm AlOx barriers critically determine device performance.
The project directly supports the development of next-generation quantum technologies, advanced photonics and energy-efficient microelectronics by improving the reliability and stability of nanoscale materials. The work will be carried out within a strong multi-partner framework.