Development of a new generation of reversible polymer adhesives
Polymeric adhesives are generally cross-linked systems used to bond two substrates throughout the lifetime of an assembly, which may be multi-material, for a wide range of applications. At their end of life, the presence of adhesives makes it difficult to separate materials and recycle them. Moreover, it is difficult to destroy the cross-linking of the adhesives without chemical or thermal treatment that is also aggressive for the bonded substrates.
In this context, the CEA is developing adhesives with enhanced recyclability, by integrating recyclability into the chemical structures right from the synthesis of the polymer networks. The first approach involves incorporating dynamic covalent bonds into polymer networks, which can be exchanged under generally thermal stimulus (e.g. vitrimers). A second approach involves synthesising polymers that can be depolymerised under a specific stimulus (self-immolating polymers) and have the ability to cross-link.
The post-doc will develop 2 networks that can be used as adhesives with enhanced recyclability. A first network will be based on a depolymerizable chemistry under stimulus already developed on linear polymer chains, to be transposed to a network. A second vitrimer network will be synthesised on the basis of previous work at the CEA. Activation of the bond exchange in this network will take place via a so-called photolatent catalyst, which can be activated by UV and will make it possible to obtain a UV- and heat-stimulated adhesive. The choice and synthesis of these catalysts and their impact on the adhesive will be the focus of the study. The catalysts obtained could also be used to trigger depolymerisation of the first depolymerisable system under stimulus.
Impact of Microstructure in Uranium Dioxide on Ballistic and Electronic Damage
During reactor irradiation, nuclear fuel pellets undergo microstructural changes. Beyond 40 GWd/tU, a High Burnup Structure (HBS) appears at the pellet periphery, where initial grains (~10 µm) fragment into sub-grains (~0.2 µm). In the pellet center, under high temperatures, weakly misoriented sub-grains also form. These changes result from energy loss by fission products, leading to defects such as dislocations and cavities. To study grain size effects on irradiation damage, nanostructured UO2 samples will be synthesized at JRC-K, using flash sintering for high-density pellets. Ion irradiation experiments will follow at JANNuS-Saclay and GSI, with structural characterizations via Raman spectroscopy, TEM, SEM-EBSD, and XRD. The postdoc project will take place at JRC-K, CEA Saclay, and CEA Cadarache under expert supervision.
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.
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.
Aerosol generation and transformation mechanisms during the fuel debris cutting at Fukushima Daiichi future dismantling
During Fukushima Daiichi nuclear reactor accident, several hundred tons of fuel debris (the mixture generated by the reactor core melting and its interaction with structural materials) have been formed. Japanese government plans to dismantle with 30 to 40 years Fukushima Daiichi nuclear power station, which implies recovering these fuel debris that are there. CEA is part to several projects aiming at mastering the risks due to aerosols generated during fuel debris cutting.
The post-doctoral work objective is to exploit the large experimental database created thanks to these projects in order to study the generation and transformation mechanisms of these cutting aerosols for both thermal and mechanical cutting. An important source of aerosol seems to be partial evaporation/condensation, close to fractional distillation. A thermodynamic modelling shall be proposed, coupled with some kinetic effects. For mechanical cutting, aerosol analyses shall be compared to fuel debris block microstructure to quantify a preferential release of some phases.
After a bibliographic study, a synthesis of the experimental results will be carried out and completed, where necessary, by chemical or crystallographic analyses. The aim will be to propose a modelling of these aerosol generation and transformation mechanisms.
The postdoctoral researcher will work within an experimental laboratory of about 20 staff within CEA IRESNE institute (Cadarache site, Southern France).