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.
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.
New generation of organic susbtrates for power conversion
Recent advances in electric motors and associated power electronics have led to a significant increase in power density requirements. This increase in power density means smaller heat exchange surfaces, which amplifies the challenges associated with dissipating the heat generated by power electronics components during operation. In fact, the lack of adequate heat dissipation causes electronic components to overheat, impacting their performance, durability, and reliability. Other issues related to cost, repairability, and thermomechanical constraints call into question traditional ceramic-based insulating thermal interfaces. It is therefore imperative to develop a new generation of heat-dissipating materials that take the system environment into account.
The objective of this thesis is to replace the ceramic substrate in power module systems, whose main role is to act as the system's dielectric layer, with a thermally conductive organic matrix composite. The current substrate has well-known limitations (fragility, poor interface, cycling limit, cost). The organic substrate must have the highest possible thermal conductivity (>3 W/m.k) in order to dissipate the heat emitted properly, while also being electrically insulating with a breakdown voltage of approximately 3kV/mm. It must also have a coefficient of thermal expansion (CTE) compatible with that of copper in order to eliminate delamination phenomena during the cycling undergone by the device during its lifetime. The innovation of the doctoral student's work will lie in the use of highly thermally conductive (nano)fillers that will be electrically insulated (insulating coating) and can be oriented in a polymer resin under external stimulus.
The development of the electrical insulating shell on the thermally conductive core will be carried out using the sol-gel method. The synthesis will be controlled and optimized in order to correlate the homogeneity and thickness of the coating with the dielectric and thermal performance of the (nano)composite. The charge/matrix interface (a potential source of phonon diffraction) will also be studied. A second part will focus on grafting magnetic nanoparticles (MNPs) onto thermally conductive (nano)fillers. Commercial MNPs will be evaluated (depending on requirements, grades synthesized in the laboratory may also be evaluated). The (nano)composites must have rheology compatible with pressing and/or injection processes.
Novel architecture and signal processing for mobile optical telecommunications
Free-Space Optical Communications (FSO) rely on transmitting data via light between two distant points, eliminating the need for fibers or cables. This approach is particularly valuable when wired connections are impractical or prohibitively expensive.
However, these links are highly susceptible to atmospheric conditions—fog, rain, dust, and thermal turbulence—which attenuate or distort the light beam, significantly degrading communication quality. Current solutions remain costly and limited, both in terms of optical compensation hardware and signal processing algorithms.
Within this framework, the thesis aims to design high-performance, robust mobile optical links capable of adapting to dynamic and disturbed environments. The study will focus on leveraging Silicon-based Optical Phased Arrays (OPAs)—a technology derived from low-cost LiDAR systems—offering a promising path toward compact, integrated, and cost-effective architectures.
The primary focus of the research will be developing advanced algorithmic approaches for signal processing and compensation. The PhD candidate will be tasked with designing a dedicated simulation environment to evaluate and validate architectural choices and algorithmic strategies before practical experimentation.
The overarching goal is to propose an integrated, flexible, and reliable architecture that ensures uninterrupted optical communication in motion, with potential applications in aerospace, space, and terrestrial domains.
Multipath-based Cooperative Simultaneous Localization & Mapping through Machine Learning
The goal of this PhD is to explore the potential of machine learning (ML) tools for simultaneous localization and mapping (SLAM) applications, while leveraging multipath radio signals between cooperative wireless devices.
The idea is to identify characteristic features of the propagation channels observed over multiple radio links, so as to jointly determine the relative positions of the mobile radio devices, as well as those of scattering objects present in their vicinity. Such radio features typically rely on the arrival times of multipath echos of the transmitted signals. The envisaged approach is expected to benefit from multipath correlation as the radio devices are moving, as well as from spatial diversity and information redundancy through multi-device cooperation. The developed solution will be evaluated on both real measurements collected with integrated Ultra Wideband devices in a reference indoor environment, and synthetic data generated with a Ray-Tracing simulator.
Possible applications of this research concern group navigation in complex and/or unknown environments (incl. fleets of drones or robots, firefighters…).
Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment
Power converters are essential in numerous applications such as industry, photovoltaic systems, electric vehicles, and data centers. Their conventional maintenance is often based on fixed schedules, leading to premature replacement of components and significant electronic waste.
This PhD project aims to develop a novel non-invasive and low-cost ultrasound-based monitoring approach to assess the state of health and remaining useful life (RUL) of power converters deployed across various industries.
The research will focus on identifying and characterizing ultrasonic signatures emitted by aging electronic components, and on developing physics-informed neural networks (PINNs) to model their degradation mechanisms. The project will combine experimental studies with advanced signal processing and AI techniques (compressed sensing), aiming to detect early signs of failure and enable predictive maintenance strategies executed locally (edge deployment).
The research will be carried out within a Marie Sklodowska-Curie Actions (MSCA) Doctoral Network, offering international training, interdisciplinary collaboration, and secondments at leading academic and industrial partners across Europe (Italy and Netherlands for this PhD offer).
Instrumented PCB for predictive maintenance
The manufacturing of electronic equipment, and more specifically Printed Circuit Boards (PCBs), represents a significant share of the environmental impact of digital technologies, which must be minimized. Within a circular economy approach, the development of monitoring and diagnostic tools for assessing the health status of these boards could feed into the product’s digital passport and facilitate their reuse in a second life. In a preventive and prescriptive maintenance perspective, such tools could extend their lifespan by avoiding unnecessary periodic replacement in applications where reliability is a priority, as well as adapting their usage to prevent premature deterioration.
This PhD proposes to explore innovative instrumentation of PCBs using ‘virtual’ sensors, advanced estimators powered by measurement modalities (such as piezoelectric, ultrasonic, etc.) that could be integrated directly within the PCBs. The objective is to develop methods for monitoring the health status of the boards, both mechanically (fatigue, stresses, deformations) and electronically.
A first step will consist of conducting a state-of-the-art review and simulations to select the relevant sensors, define the quantities to be measured, and optimize their placement. Multi-physics modeling and model reduction will then make it possible to link the data to PCB integrity indicators characterizing its health status. The approach will combine numerical modeling, experimental validations, and multiparametric optimization methods.