Modeling of the MADISON fuel irradiation device for the future JHR reactor
The Jules Horowitz Reactor (RJH), currently under construction at CEA's Cadarache site, will irradiate materials and fuels in support of the French and international nuclear industry, as well as producing radioelements for medical use. To carry out its missions, the reactor will be equipped with numerous experimental devices. In particular, the MADISON device, currently under design, will irradiate 2 or 4 fuel samples under nominal stationary or operational transient conditions. The loop is representative of light-water reactor operating conditions, with single-phase and two-phase forced convection.
The main objective of the Post-Doc is to model the MADISON device and all associated heat exchanges precisely, in order to help determine the overall heat balance during the test and thus improve the accuracy of the linear power imposed on the samples. To this end, a coupled thermal model (describing the fuel rods and device structures) / CFD thermal-hydraulic model (describing the coolant) will be established using the NEPTUNE_CFD/SYRTHES code. The modeling will be validated based on results obtained from similar modeling carried out on the ISABELLE-1 and ADELINE single-rod devices in the OSIRIS and RJH reactors. The proposed approach fits in with the logic of developing digital twins of the RJH experimental devices.
DTCO for RF & mmW Applications:Focus on Homogeneous & Heterogeneous Chiplet Hybrid Bonding Challenge
In recent years, there have been numerous technological advancements in silicon-based semiconductors. However, the limits in terms of frequency performance and power seem to have been reached, requiring the development of new type III-V devices (such as InP and GaN) that are faster, more powerful and well adapted for new RF mmW applications. For reasons of flexibility, performance, and cost, it is crucial to co-integrate these new high-performance III-V components with the more traditional silicon technologies. This is one of the major objectives of the proposed topic.
The focus will be on the design and optimisation of millimetre-wave RF circuits using 3D heterogeneous hybrid bonding assembly technology. In recent years, numerous test vehicles have been fabricated and characterised to demonstrate the advantages and disadvantages of the hybrid bonding assembly process for millimetre wave RF applications. The aim is to extend this work and focus the studies and research on real RF systems, such as millimetre-wave power amplifiers. The DTCO (Design and Technology Co-Optimisations) approach will not only enable the design of efficient 3D RF circuits, but will also allow the adaptation of different 3D design rules to make 3D hybrid bonding technology relevant for the production of millimetre-scale 3D integrated systems.
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.
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.
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.
MULTI-CRITERIA ANALYZES OF HYDROGEN PRODUCTION TECHNOLOGIES BY ELECTROLYSIS
LITEN, strongly involved in electrolysis technologies, wishes to compare via a multi-criteria analysis all electrolysis technologies currently available commercially (AEL, PEMEL), in the pre-industrialization phase (SOEL), or in R&D (AEMEL and PCCEL).
Our previous studies were based on specific use cases (fixed hypotheses on the size of the factory, the source of electricity, the technology, etc.).
The objective of this new work is to be able to position the different electrolysis technologies according to parameters which will be defined at the start of the project, these parameters being of a contextual type (e.g. number of operating hours, expected flexibility), technical ( ex yield, lifespan) or technical-economic (ex CAPEX OPEX) and environmental (ex GHG impacts, materials). The aim here will be to develop an original methodology which makes it possible to define the areas of relevance of each of the electrolysis technologies according to these parameters, depending for example on the cost of the hydrogen produced and its environmental impact
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.
Simulation of thermal transport at sub-Kelvin
Thermal management in quantum computers is an urgent and crucial task. As the number of qubits rapidly scales, more electric circuits are placed close to qubits to operate them. Joule-heating of these circuits could significantly warm the qubit device, degrading its fidelity. With intensive activity in quantum computing at Grenoble, we (CEA-LETI, Grenoble, France) are looking for an enthusiastic post-doc researcher to study thermal transport at cryogenic temperature (sub-Kelvin).
The post-doc will apply the finite-element non-equilibrium Green’s function [1], developed in the group of Natalio Mingo at CEA-Grenoble, to simulate phonon transport in various designed structures. The simulation result promotes comparison with on-going experiments and constructive discussions in order to optimize the thermal management.
[1] C. A. Polanco, A. van Roekeghem, B. Brisuda, L. Saminadayar, O. Bourgeois, and N. Mingo, Science Advances 9, 7439 (2023).
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.
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.