Robust path-following solvers for the finite element simulation of cracking in complex heterogeneous media: application to reinforced concrete structures
Path-following (or continuation) procedures are used to describe the unstable responses of structures exhibiting snap-back or snap-through phenomena. These methods consist in adapting the external load during the deformation process in order to satisfy a control constraint, by introducing an additional unknown, the load multiplier. Several variants exist depending on the controlled quantity: degrees of freedom, strain measures, or variables related to energy dissipation.
In addition to enabling the tracing of unstable responses, a major advantage of these approaches lies in improving the convergence of incremental Newton-type solvers by reducing the number of iterations required. This gain often compensates for the additional computational cost associated with the continuation algorithm. Some formulations have proven both efficient and simple to implement.
However, no objective criterion yet allows one to determine which formulation is best suited for the simulation of reinforced concrete structures, where multiple dissipation mechanisms coexist along with a strong spatial variability of the material properties.
The proposed postdoctoral work aims to develop robust path-following algorithms for such structures, building upon previous research carried out at CEA. It will include a critical analysis of existing formulations, an evaluation of their performance (monolithic or partitioned solvers), followed by their implementation. Finally, representative test cases of industrial structures will be simulated to assess the gain in robustness and computational cost compared to standard solvers.
TOMOGLASS: Gamma Emission Tomography Applied to the Radiological Characterization of Glass Residues from the Cold Crucible Vitrification Process
The TOMOGLASS project aims to develop an innovative gamma tomography system capable of operating in high-activity environments to characterize in three dimensions the glass residues resulting from the vitrification process of nuclear waste. The objective is to precisely locate platinum-group inclusions, which are poorly soluble in glass, in order to improve the understanding and control of the process. The system is based on a compact gamma imager integrating high-resolution pixelated CZT detectors, pinhole-type collimation, and mounting on a robotic arm. It will enable multi-isotopic reconstruction using advanced tomographic algorithms. This project is part of the modernization of the La Hague facilities and the integration of digital technologies within the framework of the factory of the future.
The first phase of the project will consist in demonstrating the feasibility of implementing a spectro-imager prototype in a constrained environment, building on existing technological components: detection modules and acquisition electronics based on the HiSPECT technology, and image reconstruction algorithms developed at CEA-Leti. The work will focus on conducting a multi-parameter study through numerical simulations (Monte Carlo calculation code) to design an optimized measurement system, and to generate simulated datasets for various representative measurement configurations. Once the concept has been validated, the work will continue in year N+1 with the assembly of the prototype components and their integration on a robotic arm. Experimental tests may then be carried out to demonstrate the system in a representative environment.
Tools and diagnostic methods for the reuse of electronic components
The Autonomy and Sensor Integration Laboratory (LAIC) at CEA-Leti has the primary mission of developing sensor systems for the digitalization of systems. The team's activities are at the interface of hardware (electronics, optronics, semiconductors), software (artificial intelligence, signal processing), and systems (electronic architecture, mechatronics, multiphysics modeling).
In a context of exponential growth in electronics and scarcity of resources, the reuse of electronic components from end-of-life systems represents a promising avenue to limit environmental impact and support the development of a circular economy. The objective of this project is to develop an advanced diagnostic methodology to assess the health status of electronic components, particularly power components, to reintegrate them into a less constraining second-life cycle.
The postdoctoral researcher will be tasked with developing a comprehensive approach to evaluate the reuse potential of electronic components, with the aim of reintroducing them into second-life cycles. This will include:
- Identifying relevant health indicators to monitor the performance evolution of components (e.g., MOSFETs, IGBTs, capacitors, etc.);
- Setting up test benches and sensors adapted to measure electrical, thermal, or mechanical parameters, with the goal of detecting signs of aging;
- Analyzing degradation modes through experimental tests and failure models;
- Developing algorithms for predicting the remaining useful life (RUL) adapted to different usage scenarios;
- Contributing to scientific publications, the valorization of results, and collaboration with project partners.
Modelling of Drop Settling and Coalescence in Mixer-Settlers for Liquid–Liquid Extraction
The energy transition toward low-carbon technologies—such as Li-ion batteries, photovoltaics, and wind power—relies heavily on critical materials like rare earth elements (Dy, Nd, Pr) and metals (Co, Ni, Li). However, their extraction raises serious environmental concerns, and their recycling remains limited due to their low concentrations within complex waste streams, making separation particularly challenging.
Liquid–liquid extraction stands out as an effective technique for purifying such mixtures. Yet, its industrial deployment is hindered by an incomplete understanding of the underlying physico-chemical phenomena, particularly in mixer-settlers—compact devices that combine a mixing chamber with a gravity-based settling zone. While widely used for their high efficiency and compact footprint, current models describing these systems remain semi-empirical and focus mainly on the mixing phase, limiting their predictive capabilities at larger scales. Within the framework of the French national PEPR program "Recyclability and Reuse of Materials", the CEA is leading an initiative to develop a digital twin of mixer-settlers. This postdoctoral position contributes to that project, with a focus on modeling the settler unit. The researcher will conduct experiments using well-characterized emulsions injected into a dedicated transparent mock-up, to study droplet sedimentation and track size evolution over time. These experimental data will serve to validate a model that describes the gravitational and hydrodynamic transport of droplets, as well as coalescence and break-up phenomena. Ultimately, this model will be coupled with an existing model of the mixing chamber (currently under development in a parallel PhD project), leading to the creation of a first-generation digital twin of the complete device.
VALERIAN: caracterizing electron transport for the ITkPix modules of ATLAS
A precise description of the transport of electrons and photons in matter is crucial in several of the CEA's flagship fields, notably radiation protection and nuclear
instrumentation. Their validation requires dedicated parametric studies and measurements.Given the scarcity of public experimental data, comparisons between calculation codes are also used. The challenge for the coming years is to qualify these codes in a broad energy domain, as certain discrepancies between their results have been identified during preliminary SERMA studies involving the coupled transport of neutrons, photons and electrons. The VALERIAN project involves seizing the opportunity created by a unique data collection Campaign planned for 2025-2026 at the IRFU (DRF) to better characterise these discrepancies. The IRFU has undertaken to check at least 750 pixel modules for the new trajectograph of the ATLAS experiment, as part of the rejuvenation of the large detectors at CERN. Numerous measurements with beta sources will be carried out in 2025-2026 for the qualification of these modules.
Study of the Velocity-Vorticity-Pression formulation for discretising the Navier-Stokes equations.
The incompressible Navier-Stokes equations are among the most widely used models to describe the flow of a Newtonian fluid (i.e. a fluid whose viscosity is independent of the external forces applied to the fluid). These equations model the fluid's velocity field and pressure field. The first of the two equations is none other than Newton's law, while the second derives from the conservation of mass in the case of an incompressible fluid (the divergence of velocity vanishes). The numerical approximation of these equations is a real challenge because of their three-dimensional and unsteady nature, the vanishing divergence constraint and the non-linearity of the convection term. Various discretisation methods exist, but for most of them, the mass conservation equation is not satisfied exactly. An alternative is to introduce the vorticity of the fluid as an additional unknown, equal to the curl of the velocity. The Navier-Stokes equations are then rewritten with three equations. The post-doc involves studying this formulation from a theoretical and numerical point of view and proposing an efficient algorithm for solving it, in the TrioCFD code.
Dimensionality reduction and meta-modelling in the field of atmospheric dispersion
Modelling and simulation of atmospheric dispersion are essential to ensure the safety of emissions emitted into the air by the authorized operation of industrial facilities and to estimate the health consequences of accidents that could affect these facilities. Over the past twenty years, physical dispersion models have undergone significant improvements in order to take into account the details of topography and land use that make real industrial environments complex. Although 3D models have seen their use increase, they have very significant calculation times, which hinders their use in multi-parametric studies and the assessment of uncertainties that require a large number of calculations. It would therefore be desirable to obtain the very precise results of current models or similar results in a much shorter time. Recently, we have developed a strategy consisting of reducing the dimension of distribution maps of an atmospheric pollutant obtained using a reference 3D physical model for different meteorological conditions, then having these maps learned by an artificial intelligence (AI) model which is then used to predict maps in other meteorological situations. The postdoctoral project will focus on complementing the research started by evaluating the performance of dimension reduction and model substitution methods already explored and by studying other methods. Applications will concern, in particular, the simulation of concentrations around an industrial production site that emits gaseous emissions into the atmosphere. The developments will aim to obtain an operational meta-modelling tool.
Advanced reconstruction methods for cryo-electron tomography of biological samples
Cryo-electron tomography (CET) is a powerful technique for the 3D structural analysis of biological samples in their near-native state. CET has seen remarkable advances in instrumentation in the last decade but the classical weighted back-projection (WBP) remains by far the standard CET reconstruction method. Due to radiation damage and the limited tilt range within the microscope, WBP reconstructions suffer from low contrast and elongation artifacts, known as ‘missing wedge’ (MW) artifacts. Recently, there has been a revival of interest in iterative approaches to improve the quality and hence the interpretability of the CET data.
In this project, we propose to go beyond the state-of-the-art in CET by (1) applying curvelet- and shearlet-based compressed sensing (CS) algorithms, and (2) exploring deep learning (DL) strategies with the aim to denoise et correct for the MW artifacts. These approaches have the potential to improve the resolution of the CET reconstructions and facilitate the segmentation and sub-tomogram averaging tasks.
The candidate will conduct a comparative study of iterative algorithms used in life science, and CS and DL approaches optimized in this project for thin curved structures.
Modeling of charge noise in spin qubits
Thanks to strong partnerships between several research institutes, Grenoble is a pioneer in the development of future technologies based on spin qubits using manufacturing processes identical to those used in the silicon microelectronics industry. The spin of a qubit is often manipulated with alternating electrical (AC) signals through various spin-orbit coupling (SOC) mechanisms that couple it to electric fields. This also makes it sensitive to fluctuations in the qubit's electrical environment, which can lead to large qubit-to-qubit variability and charge noise. The charge noise in the spin qubit devices potentially comes from charging/discharging events within amorphous and defective materials (SiO2, Si3N4, etc.) and device interfaces. The objective of this postdoc is to improve the understanding of charge noise in spin qubit devices through simulations at different scales. This research work will be carried out using an ab initio type method and also through the use of the TB_Sim code, developed within the CEA-IRIG institute. This last one is able of describing very realistic qubit structures using strong atomic and multi-band k.p binding models.