In-situ measurement of liquid composition by digital in-line holography
This postdoctoral position is part of the ANR ATICS project (Advanced Tri-dimensional Imaging of Complex Particulate Systems), which aims to develop a set of advanced tools and methods for modeling and reconstructing holograms to enhance the practical capabilities of three-dimensional imaging through digital inline holography. This is a collaborative research project lasting four years, involving four university laboratories, the CNRS, grandes écoles, and the CEA. Within this framework, the objective of the postdoctoral work is to provide physical knowledge and data to other team members and to demonstrate the contributions of the theoretical and numerical developments made in ATICS in two research areas in which the partners are regularly involved: multiphase flows and recycling processes. To achieve this, new experimental devices for measuring the composition of liquids will be developed, leveraging the potential of inline digital holography at various scales, from microfluidics to the study of sprays in acoustic levitation. The work will be conducted in close collaboration with the teams at the IUSTI laboratory of Aix-Marseille University.
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.
Signal processing of ultra-fast gamma-ray detectors using Machine Learning
In the frame of the ANR project AAIMME dedicated to the Positron-Emission Tomography (PET), we propose a 24-month post-doctoral position that will focus on the development of signal processing methods for the detector ClearMind, designed at the CEA-IRFU. The detector is specifically developed to provide a precise interaction time in the sensitive volume. It consists of a scintillator PbWO4 detector, coupling with a Micro Channel Plate PhotoMultiplier Tube, whose signals are digitized using fast acquisition modules SAMPIC. The main advantage is to exploit both fast Tcherenkov and scintillation photons to reconstruct as accurately as possible the interactions inside the Crystal.
The analysis of the detector signal represents a major challenge: they are complex and intricated, thus, it necessitates a dedicated processing step.
The objective of this post-doc is to develop these trustworthy Machine Learning algorithms to reconstruct the properties of the gamma-ray interaction in the detector, with the highest achievable accuracy, using the detector signals.
High-performance computing using CMOS technology at cryogenic temperature
Advances in materials, transistor architectures, and lithography technologies have enabled exponential growth in the performance and energy efficiency of integrated circuits. New research directions, including operation at cryogenic temperatures, could lead to further progress. Cryogenic electronics, essential for manipulating qubits at very low temperatures, is rapidly developing. Processors operating at 4.2 K using 1.4 zJ per operation have been proposed, based on superconducting electronics. Another approach involves creating very fast sequential processors using specific technologies and low temperatures, reducing energy dissipation but requiring cooling. At low temperatures, the performance of advanced CMOS transistors increases, allowing operation at lower voltages and higher operating frequencies. This could improve the sequential efficiency of computers and simplify the parallelization of software code. However, materials and component architectures need to be rethought to maximize the benefits of low temperatures. The post-doctoral project aims to determine whether cryogenic temperatures offer sufficient performance gains for CMOS or should be viewed as a catalyst for new high-performance computing technologies. The goal is particularly to assess the increase in processing speed with conventional silicon components at low temperatures, integrating measurements and simulations.
Adapting the Delayed Hydride Cracking (DHC) experience to irradiated materials
The objective of this study is to nuclearize the Delayed Hydride Cracking (DHC) experiment developed as part of Pierrick FRANCOIS PhD research (2020-2023). This experiment enables the reproduction of the DHC phenomenon in Zircaloy cladding under laboratory conditions to determine the material's fracture toughness in case of DHC: KI_DHC.
The term "nuclearize" refers to the adaptation of the experiment to test irradiated materials within dedicated shielded enclosures (called hot cells), where materials are handled using remote manipulators. The experimental protocols described in Pierrick FRANCOIS' thesis must therefore be modified, and ideally simplified, to allow for their implementation in hot cells. This will require close collaboration with the personnel responsible for the tests and the use of numerical simulation tools developed during the same PhD research.
The development of this hot cell procedure will be used by the postdoctoral researcher to assess the risk of HC during dry storage of spent fuel assemblies by quantifying the fracture toughness of irradiated claddings.
Design and Implementation of a Neural Network for Thermo-Mechanical Simulation in Additive Manufacturing
The WAAM (Wire Arc Additive Manufacturing) process is a metal additive manufacturing method that allows for the production of large parts with a high deposition rate. However, this process results in highly stressed and deformed parts, making it complex to predict their geometric and mechanical characteristics. Thermomechanical modeling is crucial for predicting these deformations, but it requires significant computational resources and long calculation times. The NEUROWAAM project aims to develop a precise and fast thermomechanical numerical model using neural networks to predict the physical phenomena of the WAAM process. An internship in 2025 will provide a database through thermomechanical simulations using the CAST3M software. The post-doc's objective is to develop a neural network architecture capable of learning the relationship between the manufacturing configuration and the thermomechanical characteristics of the parts. Manufacturing tests on the CEA's PRISMA platform will be conducted to validate the model and prepare a feedback loop. The CEA List's Interactive Simulation Laboratory will contribute its expertise in accelerating simulations through neural networks and active learning to reduce training time.
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