Rheology and Conduction of Functional Polymers for Embedded Electronics in 3D/4D Additive Manufacturing
This PhD project, conducted on the MAPP platform (CEA-Metz), focuses on the development of additive manufacturing (3D/4D) processes for the integration of smart materials. The aim is to overcome the limitations of traditional planar electronic architectures (PCBs, wafers) integration by enabling the direct-to-shape printing of electronic functions within 3D parts performed by Fused Deposition Modeling and Paste Extrusion Modeling. The research will address functional conductive polymers, composed of an organic matrix and metallic particles, whose conduction mechanisms (direct contact, tunneling effect, ionic conduction) are governed by the percolation threshold. The study will investigate the processing of these materials, their rheological and electrical behavior, and the exploitation of their resistive, piezoresistive and piezoelectric properties to design novel sensor (3D) and actuator (4D) functions. The doctoral candidate will benefit from advanced characterization facilities and the guidance of a multidisciplinary team with expertise in additive manufacturing, materials science, and microelectronics.
Study of new concepts for miniaturizable and parallelizable liquid-liquid extractors
In the process of developing procedures, their miniaturization represents a major challenge for upstream research and development (R&D). Indeed, the miniaturization of procedures offers numerous advantages in terms of reducing the volume of raw materials, waste management, screening possibilities, automation, and safety for personnel.
To date, the counter-current liquid-liquid extraction process does not have a convincing miniaturization solution, although the applications are numerous: in pharmacy, chemical synthesis, nuclear, or nuclear medicine.
The CEA-ISEC in Marcoule has developed new microfluidic tools to perform these operations in a simple and operational manner, based on a fine understanding of the instabilities of two-phase flows in capillaries.
This 3-year study topic proposes:
- To experiment, understand, and finely model the flows and mass transfers;
- To optimize and then transpose the phenomena to industrially significant volumes;
- To publish and participate in international conferences.
The doctoral student will benefit from learning about the world of research in a team that values quality in the supervision and future of its doctoral students, in a multidisciplinary team ranging from process engineering to instrumentation, with projects ranging from research to industry.
General competencies in chemical engineering and mass transfer are required. Competencies in collaborating with our academic partners will be essential to the success of the study project.
AI Enhanced MBSE framework for joint safety and security analysis of critical systems
Critical systems must simultaneously meet the requirements of both Safety (preventing unintentional failures that could lead to damage) and Security (protecting against malicious attacks). Traditionally, these two areas are treated separately, whereas they are interdependent: An attack (Security) can trigger a failure (Safety), and a functional flaw can be exploited as an attack vector.
MBSE approaches enable rigorous system modeling, but they don't always capture the explicit links between Safety [1] and Security [2]; risk analyses are manual, time-consuming and error-prone. The complexity of modern systems makes it necessary to automate the evaluation of Safety-Security trade-offs.
Joint safety/security MBSE modeling has been widely addressed in several research works such as [3], [4] and [5]. The scientific challenge of this thesis is to use AI to automate and improve the quality of analyses. What type of AI should we use for each analysis step? How can we detect conflicts between safety and security requirements? What are the criteria for assessing the contribution of AI to joint safety/security analysis?
Adaptive and explainable Video Anomaly Detection
Video Anomaly Detection (VAD) aims to automatically identify unusual events in video that deviate from normal patterns. Existing methods often rely on One-Class or Weakly Supervised learning: the former uses only normal data for training, while the latter leverages video-level labels. Recent advances in Vision-Language Models (VLMs) and Large Language Models (LLMs) have improved both the performance and explainability of VAD systems. Despite progress on public benchmarks, challenges remain. Most methods are limited to a single domain, leading to performance drops when applied to new datasets with different anomaly definitions. Additionally, they assume all training data is available upfront, which is unrealistic for real-world deployment where models must adapt to new data over time. Few approaches explore multimodal adaptation using natural language rules to define normal and abnormal events, offering a more intuitive and flexible way to update VAD systems without needing new video samples.
This PhD research aims to develop adaptable Video Anomaly Detection methods capable of handling new domains or anomaly types using few video examples and/or textual rules.
The main lines of research will be the following:
• Cross-Domain Adaptation in VAD: improving robustness against domain gaps through Few-Shot adaptation;
• Continual Learning in VAD: continually enriching the model to deal with new types of anomalies;
• Multimodal Few-Shot Learning: facilitating the model adaptation process through rules in natural language.
Bayesian Neural Inference Using Ferroelectric Memory Transistors
An increasing number of safety-critical systems now rely on artificial intelligence functions that must operate under strict energy constraints and in environments characterized by data scarcity and high uncertainty. However, conventional deterministic AI approaches provide only point estimates and lack principled uncertainty quantification, which can lead to unreliable or unsafe decisions in real-world deployment.
This PhD is positioned within the emerging field of Bayesian electronics, which aims to implement probabilistic inference directly in hardware by leveraging the intrinsic stochasticity of nanoscale devices to represent and manipulate probability distributions. While memristive devices have previously been explored for Bayesian inference, their limited endurance and high programming energy remain critical bottlenecks for on-chip learning.
The objective of this thesis is to investigate ferroelectric field-effect memory transistors (FeMFETs) as building blocks for hardware Bayesian neural networks. The work will involve characterizing and modeling the exploitable ferroelectric randomness for sampling and probabilistic weight updates, designing Bayesian neuron and synapse architectures based on FeMFETs, and evaluating their robustness, energy efficiency, and system-level performance for safety-critical inference under uncertainty.
Modeling the impact of defects in Steel–Concrete Structures. Identification of critical defects through metamodeling and optimization algorithms
To meet growing constructability challenges, steel–concrete (SC) structures are emerging as a promising alternative to conventional reinforced concrete structures. These elements are composed of infill concrete, two external steel plates, and steel shear studs that ensure composite action. While such structures present a clear interest due to their overall mechanical behavior, the presence of the steel plates prevents visual inspection of the concrete casting quality. It is therefore essential to characterize the impact of possible defects. This is the context of the proposed PhD research. Building upon recent results obtained in the laboratory, the goal is to develop a numerical framework to account for defects in steel–concrete structures. The thesis will be structured in several stages: validation of a modeling strategy for the mechanical behavior of defect-free SC structures, introduction of defects in the simulations and assessment of the applicability of the numerical approach, development of a metamodel and sensitivity analysis, and identification of critical defect configurations through optimization algorithms. One of the operational objectives of this doctoral work is to provide a tool capable of identifying critical defect configurations (size, position, and number) with respect to a given target quantity of interest (such as loss of strength or reduction in average stiffness). The research will therefore rely on the use and further development of state-of-the-art numerical tools in the fields of finite element modeling, optimization techniques, sensitivity analysis, and metamodeling. The thesis will be carried out within a rich collaborative environment, notably in partnership with EDF.
Proximal primal-dual method for joint estimation of the object and of unknown acquisition parameters in Computed Tomography.
As part of the sustainable and safe use of nuclear energy in the transition to a carbon-free energy future, the Jules Horowitz research reactor, currently under construction at the CEA Cadarache site, is a key tool for studying the behaviour of materials under irradiation. A tomographic imaging system will be exploited in support of experimental measures to obtain real-time images of sample degradation. This imaging system has extraordinary characteristics due to its geometry and to the size of the objects to be characterized. As a result, some acquisition parameters, which are essential to obtain a sufficient image reconstruction quality, are not known with precision. This can lead to a significant degradation of the final image.
The objective of this PhD thesis is to propose methods for the joint estimation of the object under study and of the unknown acquisition parameters. These methods will be based on modern convex optimization tools. This thesis will also explore machine learning methods in order to automate and optimize the choice of hyperparameters for the problem.
The thesis will be carried out in collaboration between the Marseille Institute of Mathematics (I2M CNRS UMR 7373, Aix-Marseille University, Saint Charles campus) and the Nuclear Measurement Laboratory of the IRESNE institute of the French Alternative Energies and Atomic Energy Commission (CEA Cadarache, Saint Paul les Durance). The doctoral student will work in a stimulating research environment focused on strategic questions related to non-destructive testing. He or she will also have the opportunity to promote his or her research work in France and abroad.
Synthesis of organic aerogels from polydicyclopentadiene derivatives
The study of inertial confinement fusion of the deuterium + tritium (DT) mixture has long been a research focus at the CEA. Experiments related to this topic, carried out within the Laser Mégajoule (LMJ) facility, require the use of materials with specific properties. This includes, among others, polymer foams (organic aerogels) used as pre-ignition targets. Such materials must combine very low density with sufficient mechanical strength to be compatible with the preparation process employed.
In this context, the objective is to develop CHx polymeric aerogels based on polydicyclopentadiene (pDCPD) and other polymers derived from ring-opening metathesis polymerization (ROMP), in order to produce materials that are (i) of low apparent density (target value in the project: below 50 mg/cc), (ii) homogeneous, (iii) exhibiting fine (open) nano-porosity, and (iv) machinable.
The proposed PhD work would focus on three main areas:
1. The synthesis of new (co-)monomers
2. The preparation of organic aerogels
3. The exploitation of data using AI (opportunity)
Effect of water radiolysis on the hydrogen absorption flux by austenitic stainless steels in the core of a nuclear pressurized water reactor
In pressurized water nuclear reactors, the core components are exposed to both corrosion in the primary medium, pressurized water at around 150 bar and 300°C, and to neutron flux. The stainless steels in the core are damaged by a combination of neutron bombardment and corrosion. In addition, radiolysis of the water can have an impact on the mechanisms and kinetics of corrosion, the reactivity of the medium and, a priori, the mechanisms and kinetics of hydrogen absorption by these materials. This last point, which remains unexplored, may prove problematic, as hydrogen in solid solution in steel can lead to changes in (and degradation of) the mechanical properties of the steel or induce premature cracking of the part. The pioneering work developed in this highly experimental thesis will focus on the impact of radiolysis phenomena on the mechanisms and kinetics of corrosion and, above all, hydrogen pick-up in 316L stainless steel exposed to the primary environment under irradiation. Hydrogen will be traced by deuterium, and neutron irradiation simulated by electron irradiation on particle accelerators. An existing permeation cell will be modified into a unique setup to allow in operando measurement by mass spectrometry of the deuterium permeation flux through a sample exposed to the simulated primary water under radiolysis conditions. The distribution of hydrogen in the material, as well as the nature of the oxide layers formed, will be analysed in detail using state-of-the-art techniques available at the CEA and in partner laboratories. The doctoral student will ultimately be required to (i) identify the mechanisms involved (corrosion and hydrogen entry), (ii) estimate their kinetics and (iii) model the evolution of hydrogen flux in the steel in connection with radiolysis activity.
Multiphysic modeling of sintering of nuclear fuel pellet: effect of atmosphere on shrinkage kinetics
Uranium dioxide (UO2) fuels used in nuclear power plants are ceramics, for which solid-phase sintering is a key manufacturing step. The sintering stage involves heat treatment under controlled partial O2 pressure that induces coarsening of UO2 grain and then consolidation and densification of the material. Grain growth induce material densification and macroscopic shrinkage of the pellet. If the green pellet (powder obtained by pressing, manufacturing step before sintering) admit a highly heterogeneous density, this gradient leading to differential shrinkage and the appearance of defects. Furthermore, the sintering atmosphere, i.e., the gas composition in the furnace, impacts grain growth kinetics and thus the shrinkage of the pellet. Advanced simulation is the key to improving understanding of the mechanisms observed as well as optimizing manufacturing cycles.
The PhD thesis aims at developing a Thermo-chemo-mechanical modeling of sintering to simulate the impact of the gas composition and properties on the pellet densification. This scale will enable us to take into account not only the density gradients resulting from pressing, but also the oxygen diffusion kinetics that have a local impact on the densification rate, which in turn impacts the transport process. Therefore, a multiphysics coupling phenomenon has to be modelled and simulated.
This thesis will be conducted within the MISTRAL joint laboratory (Aix-Marseille Université/CNRS/Centrale Marseille CEA-Cadarache IRESNE institute). The PhD student will leverage his results through publications and participation in conferences and will have gained strong skills and expertise in a wide range of academic and industrial sectors.