Preparation and characterization of an oxide/oxide composite
Fiber-reinforced ceramic matrix composites (CMCs) are a class of materials that combine good specific mechanical properties (properties relative to their density) with resistance to high temperatures (> 1000 °C), even in oxidizing atmospheres. They are typically composed of a carbon or ceramic fiber reinforcement and a ceramic matrix (carbide or oxide.
The proposed study focuses on the development of a low-matrix oxide/oxide CMC with suitable dielectric, thermal, and mechanical properties.
This study will be conducted in collaboration with several laboratories at CEA Le Ripault.
Digital correction of the health status of an electrical network
Cable faults are generally detected when communication is interrupted, resulting in significant repair costs and downtime. Additionally, data integrity becomes a major concern due to the increased threats of attacks and intrusions on electrical networks, which can disrupt communication. Being able to distinguish between disruptions caused by the degradation of the physical layer of an electrical network and an ongoing attack on the energy network will help guide decision-making regarding corrective operations, particularly network reconfiguration and predictive maintenance, to ensure network resilience. This study proposes to investigate the relationship between incipient faults in cables and their impact on data integrity in the context of Power Line Communication (PLC). The work will be based on deploying instrumentation using electrical reflectometry, combining distributed sensors and AI algorithms for online diagnosis of incipient faults in electrical networks. In the presence of certain faults, advanced AI methods will be applied to correct the state of the health of the electrical network's physical layer, thereby ensuring its reliability.
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.
Development of optoelectronic systems for quantum sensor technologies
The main mission of CEA LETI's Autonomy and Sensor Integration Laboratory (LAIC) is to develop sensor systems, and in particular quantum sensors for high-precision magnetic field measurement applications. 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). The Swarm project (https://swarm.cnes.fr/en/), which put our quantum sensors for measuring the Earth's magnetic field into orbit in 2013, is one of our track records, and a new program with similar objectives gets underway this year.
Quantum technologies are strategic for the development of sensors with unrivalled performances, as we have demonstrated in magnetometry. Our challenge today is to adapt these developments and this know-how to new physics.
To support our developments in quantum sensors, we are looking for an opto-electronics post-doc researcher to design new quantum sensors and develop the associated optical benches. This post-doc position will have a significant experimental component.
Your main mission will be to participate to the development of these new sensors and their associated characterization benches, interfacing with CEA experts in the field.
More specifically, your mission will revolve around the following actions:
• Design and assembly of quantum sensors (optical fibers, RF sources, photodetectors)
• Participation in modeling the physical phenomena involved
• Design and build the optical characterization benches
• Development of the control electronics
• Publication of results in scientific journals
• Presentation of work in international conferences
• Patents proposal
Exploring microfluidic solutions for manufacturing targets for fusion power generation
As part of a call for projects on "innovative nuclear reactors", the TARANIS project involves studying the possibility of energy production by a power laser-initiated inertial confinement fusion power plant. The current context, which encourages the development of low-carbon energies, and the fusion experiments carried out by the NIF's American teams, make it very attractive to conduct high-level research aimed at eventually producing an economically attractive energy source based on inertial fusion.
Among the many technical hurdles to be overcome, the production of fusion targets with a suitable reaction scheme compatible with energy production is a major challenge. The CEA has the know-how to produce batches of capsules containing the fusible elements of the reaction. However, the current process is not suitable for mass production of hundreds of thousands of capsules per day at an acceptable cost.
One high-potential avenue lies in the use of microfluidic devices, for which the Microfluidic Systems and Bioengineering Laboratory (LSMB) of the Health Technologies and Innovation Department (DTIS) of CEA's DRT has recognized expertise.
Modeling the corrosion behavior of stainless steels in a nitric acid media with temperature
Controlling the aging of equipment materials (mainly stainless steel) of the spent nuclear fuel reprocessing plant is the subject of constant attention. This control requires a better understanding of the corrosion phenomena of steels by nitric acid (oxidizing agent used during the recycling stages), and ultimately through their modeling.
The materials of interest are Cr-Ni austenitic stainless steels, with very low carbon content. A recent study on Si-rich stainless steel, which was developed with the aim of improving the corrosion resistance of these steels with respect to highly oxidizing environments [1 , 2 ]; showed that the corrosion of this steel was thermally activated between 40 °C and 142 °C with different behavior below and above the boiling temperature (107 °C) of the solution [3]. Indeed, between 40°C and 107°C, the activation energy is 77 kJ/mol and above boiling point, it is much lower and is worth 20 kJ/mol. This difference may be due to a lower energy barrier or a different kinetically limited step.
The challenge of this post-doctoral subject is to have a predictive corrosion model depending on the temperature (below and beyond boiling). With this objective, it will be important to analyze and identify the species involved in the corrosion process (liquid and gas phase) as a function of temperature but also to characterize the boiling regimes. This model will be able to explain the difference in activation energies of this Si-rich steel below and above the boiling temperature of a concentrated nitric acid solution but will also make it possible to optimize the processes of the factory where temperature and/or heat transfer play an important role.
Development of noise-based artifical intellgence approaches
Current approaches to AI are largely based on extensive vector-matrix multiplication. In this postdoctoral project we would like to pose the question, what comes next? Specifically we would like to study whether (stochastic) noise could be the computational primitive that the a new generation of AI is built upon. This question will be answered in two steps. First, we will explore theories regarding the computational role of microscopic and system-level noise in neuroscience as well as how noise is increasingly leveraged in machine leaning and artificial intelligence. We aim to establish concrete links between these two fields and, in particular, we will explore the relationship between noise and uncertainty quantification.
Building on this, the postdoctoral researcher will then develop new models that leverage noise to carry out cognitive tasks, of which uncertainty is an intrinsic component. This will not only serve as an AI approach, but should also serve as a computational tool to study cognition in humans and also as a model for specific brain areas known to participate in different aspects of cognition, from perception to learning to decision making and uncertainty quantification.
Perspectives of the postdoctoral project should inform how future fMRI imaging and invasive and non-invasive electrophysiological recordings may be used to test theories of this model. Additionally, the candidate will be expected to interact with other activates in the CEA related to the development of noise-based analogue AI accelerators.
Study of the specific features of highly distributed architectures for decision and control requirements
Our electricity infrastructure has undergone and will continue to undergo profound changes in the coming decades. The rapid growth in the share of renewables in electricity generation requires solutions to secure energy systems, especially with regard to the variability, stability and balancing aspects of the electricity system and the protection of the grid infrastructure itself. The purpose of this study is to help design new decision-making methods, specially adapted to highly distributed control architectures for energy networks. These new methods will have to be evaluated in terms of performance, resilience, robustness and tested in the presence of various hazards and even byzantines.
Study of the seismic behavior of piping systems using mechanical models of different degrees of fidelity
Piping systems are part of the equipment to which particular attention is paid as part of the safety review or design of nuclear installations. They are designed in accordance with codes, standards and regulations to withstand loads that occur or may occur over the life of a facility. These systems must therefore be designed to withstand accidental loads such as earthquakes. Feedback shows that piping systems generally behave well in the event of an earthquake. When failures are observed, they are more likely to be due to significant anchor movement, brittle materials, unwelded joints, corrosion, piping support failures, or seismic interactions. In practice, to be able to estimate the beyond design seismic behavior and the associated failure risks, the engineer can implement numerical models involving varying degrees of refinement depending on needs. This study consists of taking stock of the numerical modeling capabilities of piping systems under earthquake. For reasons of computational burden, global modeling based on beam elements is often favored, considering simplified material laws such as bilinear material laws with kinematic hardening. We know the “theoretical” limits of these models but it is difficult to have clear ideas about their real limits of applicability depending on the level of loading and the damage targeted. To make this assessment, we propose to interpret, using different numerical models involving different degrees of fidelity, the results of the experimental campaign carried out by the BARC and which was used for the MECOS benchmark (METallic COmponent margins under high Seismic loads).