Solving electromagnetic integral equations: from high-order discretization to H-matrix compression
The simulation of electromagnetic (EM) wave problems plays a key role in many fields, ranging from object characterization and inspection to radar stealth. A common approach consists in transforming the initial 3D volumetric problem into a 3D surface integral equation defined at material interfaces and discretizing it using a Boundary Element Method (BEM). High-order (HO) discretization schemes accelerate approximation convergence and thus provide a significant gain in accuracy for a given number of mesh elements, but complicates the assembly of the linear system. In particular, the calculation of singular and quasi-singular integrals becomes challenging, while the integration of HO methods into fast compression algorithms based on the hierarchical matrix formalism (HMAT) raises questions about the overall efficiency of the BEM. This postdoctoral position aims to address these in order to obtain a solution that is both efficient and robust for the targeted EM applications.
Instrumentation and Physics-Informed 3D Reconstruction for Quantum Magnetic Diagnosis of Li-ion Batteries
The OPTIMAQ project of the CEA aims to reduce the reject rate in Li-ion cell formation — currently up to 50%, with a target of 5% — through a non-invasive quantum magnetic measurement system. This post-doctoral position covers both the software and experimental strands of the project: setting up the measurement bench (helium-4 optically pumped magnetometer, shielding, cell translation system) and developing a physics-informed 3D current density reconstruction algorithm (RBF-FD/PINN) to detect defects as early as the formation stage.
Hosted at CEA-LETI in Grenoble within the LSSC laboratory (sensor signal processing, reconstruction), in close collaboration with the L2EP/LAIC laboratories (battery system instrumentation, quantum magnetometry) at CEA-LETI and the LAPS/LMPS laboratories (Li-ion electrochemistry: experimental implementation and testing, modeling and simulation) at CEA-LITEN, this position is part of a 24-month project targeting TRL 3, with prospects for publications and patents.
Artificial Intelligence for Rapid Source Term Estimation (AIFORSTE)
The AIFORSTE project of the CEA aims to improve emergency management for atmospheric releases resulting from industrial accidents or malicious acts. We will develop a source estimation method operating in near real-time, enabling a fast and effective response to these critical events where time is always a fundamental constraint.
Given the impossibility of using detailed CFD simulations for immediate intervention, and the inaccuracy of simplified analytical models, this postdoctoral position will be devoted to a hybrid approach based on Physics-Informed Artificial Intelligence.
The core of the work will consist in designing and optimizing deep neural network architectures combining PINNs (Physics-Informed Neural Networks) and Neural Operators (FNO, DeepONet). The objective will be to quasi-instantaneously evaluate the position, intensity, and kinetics of one or several unknown release sources from a limited number of noisy measurements, while respecting the physical conservation laws.
Hosted at CEA-Leti (Grenoble) and co-supervised by CEA-DAM, the project will apply to realistic case studies (urban districts, industrial sites) using data generated through high-fidelity simulation. This 18-month project offers a unique opportunity to publish in leading journals and to help build strategic technological autonomy in a field where security and environmental monitoring are of major importance.
Development of a neutron imaging simulation software applied to NDT
CEA-List is developing CIVA, a reference platform for non-destructive testing (NDT) simulations, particularly X-ray radiography using the Monte Carlo method.
The project aims to extend these capabilities to neutron imaging, which complements X-ray radiography due to its different sensitivity to chemical elements. The goal is to develop the necessary digital tools for industrial neutron imaging simulation, with an eventual link to an experimental platform.
The post-doctoral researcher will first work on implementing a simplified neutron imaging model for thermal and fast neutrons. Next, they will account for scattering effects using the Monte Carlo method, in collaboration with CEA DES. Finally, experimental validation will assess the model's ability to reproduce experimental observations, using existing data and data to be acquired on large instruments.
The project is cross-cutting across CEA-List/DRT, DES, and DRF, with exchanges focusing on the comparison of different neutron simulation codes.
Cryo-CMOS electronics: Thermal effects and electrical performance in FDSOI MOSFETs down to very low temperature
The post-doctoral subject focuses on studying thermal effects and electrical performance in FDSOI MOSFET transistors down to very low temperature for cryogenic applications, such as quantum computers and space applications. The goal is to model and characterize STMicroelectronics' 28FDSOI technology down to 4K and below, concentrating on self-heating and its impacts on circuit performance. The work includes DC and RF measurements, studying the back bias effect, exploring thermal couplings, and associated modeling. The project also aims to integrate these models into a 4K-valid Process Design Kit (PDK) to optimize circuits operating at very low temperatures. This work is part of the IRT Qloop project, in collaboration with STMicroelectronics. The results will contribute to advancing Cryo-CMOS electronics and the development of high-performance quantum computers.
X-ray metrology and spectrometry for medical imaging
This 12-month postdoctoral position focuses on X-ray metrology and spectrometry for medical imaging. The context is the need for beam-quality traceability, since current standards (IEC 61267) do not cover the new filtration combinations (silver, gold, tin) used in spectral computed tomography (sCT), a technique involved in over 60 million CT exams annually in Europe. The position comprises two independent strands. The first (tasks 1-3) covers drafting methodological guides, qualifying LNHB's two spectrometry benches (CdTe and HPGe), establishing new reference radiation conditions, on-site clinical measurements, and participation in an inter-laboratory comparison. The second strand (tasks 4-5) concerns an instrumented dosimeter dedicated to radiological imaging, stemming from a thesis currently being finalized: pre-series fabrication, calibration, full metrological characterization, clinical validation in a hospital setting, and building a demonstrator.
Exploring the atomic configuration space with generative AI for the simulation of chemically disordered nuclear materials
How do you predict a material's properties when the number of possible atomic configurations exceeds 2^2500? That is the bottleneck our IRESNE (nuclear fuel physics) and LIST (AI) teams have just cracked with PULSE, a generative (VAE) method published in Nature Scientific Reports, already cutting computational cost by more than two orders of magnitude (22,282 CPU hours down to 85 on a test case). With no known equivalent in the international literature, PULSE positions CEA as a pioneer in generative sampling of the configuration space of chemically disordered materials.
This 24-month postdoc gives you the opportunity to drive this method toward its next generation, leading three ambitious, parallel research axes: pushing model accuracy on systems of several thousand atoms with an IWAE architecture; equipping it with the ability to quantify its own uncertainty — a prerequisite for any use in nuclear safety; and, in the second year, tackling a high-value exploratory axis — generalizing PULSE to a continuous latent space, opening the door to any disordered crystal or alloy.
You will work at the heart of an all-CEA consortium bringing together two complementary strengths — atomistic nuclear fuel physics at IRESNE and state-of-the-art generative AI at LIST — with access to CEA supercomputers, the freedom to publish in top-tier journals, and the prospect of seeing your results feed directly into reactor safety analyses through the PLEIADES platform. A position built for a curious mind who wants to combine cutting-edge generative AI research with concrete impact on a strategic nuclear-energy challenge.
Advanced fuzzing for software supply-chain security
IoT devices (routers, video surveillance systems, etc.) rely on binary code to operate. This code often incorporates thousands of pre-existing software components, mostly drawn from open-source libraries whose code is freely accessible online. This complexity opens the door to software supply chain attacks, notably through the insertion of backdoors or the exploitation of known vulnerabilities.
The SECUBIC project aims to enhance the detection of these vulnerabilities within IoT firmware. In this context, the candidate will contribute to deepening existing research work and will take part in the development of new fuzzing and static analysis techniques designed to prevent and detect such attacks.
Thermal properties of 3D aluminum nitride structures for electronic packaging
The 12-month postdoctoral fellowship is part of the overall 3DNAMIC project, funded by the Occitanie region and involving the Materials platform of the DRTDOCC department and the Laplace laboratory. A thesis began in December 2024 aimed at “the study and characterization of 3D aluminum nitride ceramics for the thermal packaging and management of electronic components.”
The postdoc is scheduled to begin at approximately in September 2026, with the following main objectives:
Objective 1: Perform a comparative analysis of the thermal properties of ceramics produced by AF elements and on model structures using different materials available in the CEA materials platform.
Objective 3: Propose, qualify, and validate, numerically and then experimentally, heat dissipation structures for ceramics obtained by FA as part of the 3DNAMIC project.
Integrating dynamic CRDTs replicas
Existing modeling frameworks have limited collaboration capabilities. Collaboration at the model level is one of the top desired features. However, most solutions rely on cloud-based and centralized databases as their technological solution. While these solutions ease collaboration among connected partners by employing concurrency control techniques, they do not support disconnected collaboration scenarios, which is an important feature for designing local-first software. This situation presents a significant compromise: utilizing cloud-based solutions with loss of data ownership control versus adopting separate instances without collaborative capabilities.
The objective of this postdoctoral project is to contribute to and extend an existing local-first Model-Based Systems Engineering (MBSE) framework, built upon specialized Conflict-free Replicated Data Types (CRDTs). The goal is to enable real-time collaboration through modeling-specific CRDTs. The proposed approach involves extending a middleware communication layer utilizing CRDTs to
seamlessly synchronize distributed, offline-capable engineering models.
The postdoctoral researcher will conduct a state-of-the-art review of communication and group membership approaches in P2P environments. One major issue to be taken into account is the entry and exit of members in a group, so the CRDT state is always
coherent. The components will be integrated into our CRDT and modeling framework.