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.
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.
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.
Numerical simulation of turbulence models on distorted meshes
Turbulence plays an important role in many industrial applications (flow, heat transfer, chemical reactions). Since Direct Simulation (DNS) is often an excessive cost in computing time, Reynolds Models (RANS) are then used in CFD (computational fluid dynamics) codes. The best known, which was published in the 70s, is the k - epsilon model.
It results in two additional non-linear equations coupled to the Navier-Stokes equations, describing the transport, for one, of turbulent kinetic energy (k) and, for the other, of its dissipation rate (epsilon). ). A very important property to check is the positivity of the parameters k and epsilon which is necessary for the system of equations modeling the turbulence to remain stable. It is therefore crucial that the discretization of these models preserves the monotony. The equations being of convection-diffusion type, it is well known that with classical linear schemes (finite elements, finite volumes, etc ...), the numerical solutions are likely to oscillate on distorted meshes. The negative values of the parameters k and epsilon are then at the origin of the stop of the simulation.
We are interested in nonlinear methods allowing to obtain compact stencils. For diffusion operators, they rely on nonlinear combinations of fluxes on either side of each edge. These approaches have proved their efficiency, especially for the suppression of oscillations on very distorted meshes. We can also take the ideas proposed in the literature where it is for example described nonlinear corrections applying on classical linear schemes. The idea would be to apply this type of method on the diffusive operators appearing in the k-epsilon models. In this context it will also be interesting to transform classical schemes of literature approaching gradients into nonlinear two-point fluxes. Fundamental questions need to be considered in the case of general meshes about the consistency and coercivity of the schemes studied.
During this thesis, we will take the time to solve the basic problems of these methods (first and second year), both on the theoretical aspects and on the computer implementation. This can be done in Castem, TrioCFD or Trust development environments. We will then focus on regular analytical solutions and application cases representative of the community.
Staggered schemes for the Navier-Stokes equations with general meshes
The simulation of the Navier-Stokes equations requires accurate and robust numerical methods that
take into account diffusion operators, gradient and convection terms. Operational approaches have
shown their effectiveness on simplexes. However, in some models or codes
(TrioCF, Flica5), it may be useful to improve the accuracy of solutions locally using an
error estimator or to take into account general meshes. We are here interested in staggered schemes.
This means that the pressure is calculated at the centre of the mesh and the velocities on the edges
(or faces) of the mesh. This results in methods that are naturally accurate at low Mach numbers .
New schemes have recently been presented in this context and have shown their
robustness and accuracy. However, these discretisations can be very costly in terms of memory and
computation time compared with MAC schemes on regular meshes
We are interested in the "gradient" type methods. Some of them are based on a
variational formulation with pressure unknowns at the mesh centres and velocity vector unknowns on
the edges (or faces) of the cells. This approach has been shown to be effective, particularly in terms of
robustness. It should also be noted that an algorithm with the same degrees of freedom as the
MAC methods has been proposed and gives promising results.
The idea would therefore be to combine these two approaches, namely the "gradient" method with the same degrees of freedom as MAC methods. Initially, the focus will be on recovering MAC schemes on regular meshes. Fundamental
questions need to be examined in the case of general meshes: stability, consistency, conditioning of
the system to be inverted, numerical locking. An attempt may also be made to recover the gains in
accuracy using the methods presented in for discretising pressure gradients.
During the course of the thesis, time will be taken to settle the basic problems of this method (first and
second years), both on the theoretical aspects and on the computer implementation. It may be carried
out in the Castem, TrioCFD, Trust or POLYMAC development environments. The focus will be on
application cases that are representative of the community.
Development of a transport chemistry model for spent fuel in deep geological disposal under radiolysis of water
The direct storage of spent fuel (SF) represents a potential alternative to reprocessing as a means of managing nuclear waste. The direct storage of spent fuel in a deep geological environment presents a number of scientific challenges, primarily related to the necessity of developing a comprehensive understanding of the processes involved in the dissolution and release of radionuclides. The objective of this thesis is to develop a comprehensive scientific model that can accurately describe the intricate physico-chemical processes involved, such as the radiolysis of water and the interaction between irradiated fuel and its surrounding environment. The objective is to propose an accurate reactive transport model to enhance long-term predictions of storage performance. This thesis employs a back-and-forth process between modeling and experimentation, with the goal of refining the understanding of alteration mechanisms and validating hypotheses with experimental data. Based on existing models, such as the operational radiolytic model, the work will propose improvements to reduce the current simplifying assumptions. The candidate will contribute to major industrial and societal issues related to nuclear waste management and will help to provide solutions to the associated safety issues.
Hybrid CPU-GPU Preconditioning Strategies for Exascale Finite Element Simulations
Exascale supercomputers are based on heterogeneous architectures that combine CPUs and GPUs, making it necessary to redesign numerical algorithms to fully exploit all available resources. In large-scale finite element simulations, the solution of linear systems using iterative solvers and algebraic multigrid (AMG) preconditioners remains a major performance bottleneck.
The objective of this PhD is to study and develop hybrid preconditioning strategies adapted to such heterogeneous systems. The work will investigate how multilevel and AMG techniques can be structured to efficiently use both CPUs and GPUs, without restricting computations to a single type of processor. Particular attention will be paid to data distribution, task placement, and CPU–GPU interactions within multilevel solvers.
From a numerical point of view, the research will focus on the analysis and construction of multilevel operators, including grid hierarchies, intergrid transfer operators, and smoothing procedures on avalible GPU's and CPU's. The impact of these choices on convergence, spectral properties, and robustness of preconditioned iterative methods will be studied. Mathematical criteria guiding the design of efficient hybrid preconditioners will be investigated and validated on representative finite element problems, e.g., regional-scale earthquake analysis.
These developments will be coupled with domain decomposition and parallelization strategies adapted to heterogeneous architectures. Particular attention will be paid to CPU–GPU data transfers, memory usage, and the balance between compute-bound and memory-bound kernels. The interaction between numerical choices and hardware constraints, such as CPU and GPU memory hierarchies, will be designed and developed to ensure scalable and efficient implementations.
A macroscale approach to evaluate the long-term degradation of concrete structures under irradiation
In nuclear power plants, the concrete biological shield (CBS) is designed to be very close of the reactor vessel. It is expected to absorb radiation and acts as a load-bearing structure. It is thus exposed during the lifetime of the plant to high level of radiations that can have consequences on the long term. These radiations may result especially in a decrease of the material and structural mechanical properties. Given its key role, it is thus necessary to develop tools and models, to predict the behaviors of such structures at the macroscopic scale.
Based on the results obtained at a lower scale - mesoscopic simulations, from which a better understanding of the irradiation effect can be achieved and experimental results which are expected to feed the simulation (material properties especially), it is thus proposed to develop a macroscopic methodology to be applied to the concrete biological shield. This approach will include different phenomena, among which radiation-induced volumetric expansion, induced creep, thermal defromations and Mechanical loading.
These physical phenomena will be developed within the frame of continuum damage mechanics to evaluate the mechanical degradation at the macroscopic scale in terms of displacements and damage especially. The main challenges of the numerical developments will be the proposition of adapted evolution laws, and particularly the coupling between microstructural damage and damage at the structural level due to the stresses applied on the structure.
Development of a new numerical scheme, based on T-coercivity, for discretizing the Navier-Stokes equations.
In the TrioCFD code, the discretization of the Navier-Stokes equations leads to a three-step algorithm (see Chorin'67, Temam'68): velocity prediction, pressure solution, velocity correction. If an implicit time discretization scheme is to be used, the pressure solution step is particularly costly. Thus, most simulations are performed using an explicit time scheme, for which the time step depends on the mesh size, which can be very restrictive. We would like to develop an implicit time discretization scheme using a stabilized formulation of the Navier-Stokes problem based on explicit T-coercivity (see Ciarlet-Jamelot'25). It would then be possible to solve an implicit scheme directly without a correction step, which could significantly improve the performance of the calculations. This would also allow the use of the P1-P0 finite element pair, which is frugal in terms of degrees of freedom but unstable for a classical formulation.