Advanced methods of blockwise diffusion imaging for studying fetal cerebral development at the mesoscopic scale
The second half of pregnancy is an extremely rich period in terms of brain development, during which key processes such as neurogenesis, neuronal migration, and axonal growth take place; transient structures form and disappear, while brain volume increases more than tenfold. A blockwise ex-vivo imaging technique recently developed in NeuroSpin allows us to take a new look on developing brain tissues, leveraging ultra-high-field MRI at 11.7 teslas to acquire unprecedented whole-brain images at mesoscopic resolution (100 to 200 µm 3D isotropic) . The acquired data is highly multiparametric, including quantitative T1, T2, and T2* mapping, as well as high angular resolution, multi-shell diffusion-weighted imaging (b = 1500, 4500, 8000 s/mm² with 25, 60, and 90 directions respectively) at 200 µm isotropic resolution.
In order to reach such a high level of detail, a small-bore scanner is used (5 cm usable diameter) over extended scanning times (150 hours per field of view). Brains older than about 20 gestational weeks are too large, and are sectioned into blocks whose size is compatible with the scanner. The resulting blockwise images are registered using a dedicated semi-automatic protocol, and fused to reconstruct a set of whole-brain images. While this protocol has allowed us to obtain good-quality images on several fetal brain specimens (3 published, 3 other brains in progress as of the end of 2025), the diffusion imaging data remains to be fully analyzed: indeed, the blockwise nature of the acquisitions poses unique challenges, notably due to the discontinuity at the boundary between blocks, but also to non-linear image deformations and non-linearity of the magnetic field gradients.
The PhD candidate will be hosted in the inDEV team (imaging neurodevelopmental phenotypes) in close collaboration (co-supervision) with the Ginkgo team, which has leading expertise in diffusion imaging methods and has pioneered the blockwise acquisition technique in an adult brain known as Chenonceau. The PhD work lies at the interface between imaging, algorithmics, and developmental neuroscience: it will include developing and benchmarking new methods for processing this blockwise diffusion MRI to obtain high-quality tractography and fit diffusion microstructural models. It will also include an experimental part, where the PhD candidate will take part in the acquisition and reconstruction of new brains, both typical specimens and pathological ones with agenesis of the corpus callosum. Finally, the candidate will explore neuroscientific outcomes of this unprecedented dataset, which has exceptional potential to describe processes such as the development of subcortical pathways and associative white matter fibre tracts, and to become the first atlas of the developing fetal brain with fibre architecture at the mesoscopic scale.
Chemo-mechanical modeling of the coupling between carbonation, rebar corrosion and cracking in cementitious materials
Rebar corrosion is one of the main causes of premature degradation of concrete infrastructures, including in the nuclear sector, where concrete is extensively used in containment structures and waste storage facilities. Carbonation, caused by the penetration of CO2 into the concrete, lowers the pH of the pore solution, promoting rebar corrosion. This corrosion leads to the formation of expansive products that can cause cracking in the material. The proposed thesis work, developed as part of a European collaborative project between CEA Saclay, École des Mines de Paris - PSL, and IRSN, aims to develop a numerical model to simulate these phenomena. The model combines a reactive transport code (Hytec) and a finite element code (Cast3M) to study the local effects of carbonation-induced corrosion on concrete cracking. This project will benefit from parallel experimental work to gather data for parameter identification and model validation. The first part of the research will focus on modeling the carbonation of cementitious materials under unsaturated conditions, while the second part will address the corrosion of rebar caused by the pH drop induced by carbonation. The model will describe the growth of corrosion products and their expansion, inducing stress within the concrete and potential microcracking.
This research project is aimed at a PhD student wishing to develop their skills in materials science, with a strong focus on multi-physical and multi-scale modeling and numerical simulations. The thesis will be carried out principally at CEA Saclay and at École des Mines de Paris – PSL (Fontainebleau).
Ultra-low frequency wireless power transmission for sensor node charging
Wireless power transfer (WPT) technologies are rapidly expanding, particularly for wireless charging of everyday electronic devices and for powering wireless communicating sensor nodes. However, their transmission ranges remain limited, and the high operating frequencies typically used prevent energy transfer in the presence of, or through, conductive media (such as metallic barriers or seawater). This constraint significantly limits their adoption in complex environments (industrial, biomedical, etc.).The ultra-low-frequency technology investigated in our laboratory is based on an electromechanical receiver system comprising a coil and a magnet set into motion by a remotely generated magnetic field. The objective of this PhD project is to propose and develop novel ultra-low-frequency concepts to increase transmission range while maintaining sufficient power density for supplying sensor systems. The work will therefore involve studying, designing, optimizing, and experimentally validating the performance of new topologies (emitter field shaping, receiver geometries and materials, etc.). The candidate will develop analytical and numerical models to identify key system parameters and compare performance with the state of the art (range, power density, sensitivity to orientation). In addition, the candidate will propose, design, and experimentally evaluate innovative energy conversion electronics, on the transmitter and/or receiver side, to assess their impact on the overall system performance. A joint optimization of the electromechanical system and its associated power electronics will ultimately lead to the realization of a high-performance wireless power transfer system. A multidisciplinary profile with a strong orientation toward physics and mechatronics is sought for this PhD project. In addition to solid theoretical foundations, the PhD candidate must demonstrate the ability to work effectively in a team environment as well as a strong aptitude for experimental work. The PhD candidate will be integrated into the Systems Department of CEA-Leti, within a team of researchers with strong expertise in the development and optimization of electronic and mechatronic systems, combining innovative solutions for energy harvesting, wireless power transfer, low-power electronics, and sensor integration aimed at the development of autonomous systems.
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.
Assessment of new models for the investigation of hypothetical accidents in GEN4 fast reactors.
Multi-component two-phase flows in conjunction with fluid-structure interaction (FSI) problems can occur in a very large variety of engineering applications; amongst them, the hypothetical severe accidents postulated in Generation IV sodium and lead fast-breeder reactors (respectively SFR and LFR).
In SFRs, the worst postulated severe accident is the so-called hypothetical core disruptive accident (HCDA), in which the partial melt of the core of the reactor interacts with the surrounding sodium and creates a high-pressure gas bubble, the expansion of which generates shock waves and is responsible of the motion of liquid sodium, thus eventually damaging internal and surrounding structures.
The LFR presents the advantage that, unlike sodium, lead does not chemically react with air and water and, therefore, is explosion-proof and fire-safe. On the one hand, this allows a steam generator inside the primary coolant. On the other hand, the so-called steam generator tube ruptures (SGTR) should be investigated to guarantee that, in the case of this hypothetical accident the structure integrity is preserved. In the first stage of a SGTR, it is supposed that the steam-generator high-pressure high-temperature water penetrates inside the primary containment, thus generating a BLEVE (boiling liquid expanding vapor explosion) with the same behavior and consequences as the high-pressure gas bubble of a HCDA.
In both HCDA and STGR, there are situations in which the multi-component two-phase flows is in low Mach number regime which, when studied with classical compressible solver, presents problems of loss of accuracy and efficiency. The purpose of this PhD is
* to design a multiphase solver, accurate and robust, to investigate HCDA STGR scenarios.
* to design a low Mach number approach for bubble expansion problem, based on the artificial compressibility method presented in the recent paper "Beccantini et al., Computer and fluids 2024".
The aspect FSI will be also taken into account.
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.
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.