detection of multiplets and application to turkey-Syria seismic crisis of february 2023

The correlation technique, or template matching, applied to the detection and analysis of seismic events has demonstrated its performance and usefulness in the processing chain of the CEA/DAM National Data Center. Unfortunately, this method suffers from limitations which limit its effectiveness and its use in the operational environment, linked on the one hand to the computational cost of massive data processing, and on the other hand to the rate of false detections that could generate low-level processing. The use of denoising methods upstream of processing (example: deepDenoiser, by Zhu et al., 2020), could also increase the number of erroneous detections. The first part of the research project consists of providing a methodology aimed at improving the processing time performance of the multiplets detector, in particular by using information indexing techniques developed in collaboration with LIPADE (L-MESSI method , Botao Peng, Panagiota Fatourou, Themis Palpanas. Fast Data Series Indexing for In-Memory Data. International Journal on Very Large Data Bases (VLDBJ) 2021). The second part of the project concerns the development of an auto-encoder type “filtering” tool for false detections built using machine learning. The Syria-Turkey seismic crisis of February 2023, dominated by two earthquakes of magnitude greater than 7.0, will serve as a learning database for this study.

Modeling of charge noise in spin qubits

Thanks to strong partnerships between several research institutes, Grenoble is a pioneer in the development of future technologies based on spin qubits using manufacturing processes identical to those used in the silicon microelectronics industry. The spin of a qubit is often manipulated with alternating electrical (AC) signals through various spin-orbit coupling (SOC) mechanisms that couple it to electric fields. This also makes it sensitive to fluctuations in the qubit's electrical environment, which can lead to large qubit-to-qubit variability and charge noise. The charge noise in the spin qubit devices potentially comes from charging/discharging events within amorphous and defective materials (SiO2, Si3N4, etc.) and device interfaces. The objective of this postdoc is to improve the understanding of charge noise in spin qubit devices through simulations at different scales. This research work will be carried out using an ab initio type method and also through the use of the TB_Sim code, developed within the CEA-IRIG institute. This last one is able of describing very realistic qubit structures using strong atomic and multi-band k.p binding models.

Modeling SiGe based spin qubits

The CEA is developing an original spin qubit platform based on "silicon-on-insulator" (SOI) technology and is now turning to new pathways in Si/SiGe (electrons) and Ge/SiGe (holes). This activity is carried out by a consortium bringing together three of major laboratories in Grenoble: CEA-IRIG, CEA-LETI and CNRS-Néel. On this SOI platform, Grenoble has, for example, demonstrated the electrical manipulation of a single electron spin, as well as the first hole spin qubit, and recently obtained record lifetimes and spin-photon coupling for hole spins. In this context, it is essential to support the development of these advanced quantum technologies with advanced theory and modeling. CEA-IRIG is actively developing TB_Sim code. TB_Sim is able of describing highly realistic qubit structures down to the atomic scale if needed, using atomistic strong bonding models and multi-band k.p models for the electronic structure of materials. Using TB_Sim, CEA has recently examined various aspects of spin qubit physics, in close collaboration with experimental groups in Grenoble and with CEA partners in Europe. The first objective of this research work is to strengthen our understanding of electron spin qubits based on Si/SiGe heterostructures through analytical modeling as well as advanced numerical simulation using TB_Sim. The second objective is to compare the performances of the Si/SiGe platform to other Ge/SiGe and Si MOS platforms to identify its strengths and weaknesses.

Design of a high-energy phase contrast radiography chain

As part of hydrodynamic experiments carried out at CEA-DAM, the laboratory is seeking, using pulsed X-ray imaging, to radiograph thick objects (several tens of mm), made of low-density materials (around 1 g/cm3), inside which shock waves propagate at very high speeds (several thousand m/s). For this type of application, it is necessary to use energetic X-ray sources (beyond 100 keV). Conventional X-ray imaging, which provides contrast due to variations in absorption cross sections, proves insufficient to capture the small density variations expected during the passage of the shock wave. A theoretical study recently carried out in the laboratory showed that the complementary exploitation of the information contained in the X-ray phase should enable better detectability. The aim of the post-doctorate is to provide experimental proof of concept for this theoretical study. For greater ease of implementation, the work will mainly focus on the dimensioning of a static X-ray chain, where the target is stationary and the source emits continuous X-ray radiation. Firstly, the candidate will have to characterize in detail the spectrum of the selected X-ray source as well as the response of the associated detector. In a second step, he (she) will design and have manufactured interference gratings adapted to high-energy phase measurements, as well as a representative model of the future moving objects to be characterized. Finally, the student will carry out radiographic measurements and compare them with predictive simulations. The student should have a good knowledge of radiation-matter interaction and/or physical and geometric optics. Proficiency in object-oriented programming and/or the Python and C++ languages would be a plus.

Optimization of a metrological approach to radionuclide identification based on spectral unmixing

The Laboratoire national Henri Becquerel (LNE-LNHB) at CEA/Saclay is the laboratory responsible for French references in the field of ionizing radiations. For several years now, it has been involved in the development of an automatic analysis tool for low-statistics gamma spectra, based on the spectral unmixing technique. This approach makes it possible to respond to metrological constraints such as robust decision-making and unbiased estimation of counts associated with identified radionuclides. To extend this technique to field measurements, and in particular to the deformation of spectra due to interactions in the environment of a radioactive source, a hybrid spectral unmixing model combining statistical and automatic learning methods is currently being developed. The aim of this mathematical solution is to implement a joint estimation of the spectra measured and the counts associated with the radionuclides identified. The next step will be to quantify the uncertainties of the quantities estimated from the hybrid model. The aim is also to investigate the technique of spectral unmixing in the case of neutron detection with a NaIL detector. The future candidate will contribute to these various studies in collaboration with the Laboratoire d'ingénierie logicielle pour les applications scientifiques (CEA/DRF).

Development of Algorithms for the Detection and Quantification of Biomarkers from Voltammograms

The objective of the post-doctoral research is to develop a high-performance algorithmic and software solution for the detection and quantification of biomarkers of interest from voltammograms. These voltammograms are one-dimensional signals obtained from innovative electrochemical sensors. The study will be carried out in close collaboration with another laboratory at CEA-LIST, the LIST/DIN/SIMRI/LCIM, which will provide dedicated and innovative electrochemical sensors, as well as with the start-up USENSE, which is developing a medical device for measuring multiple biomarkers in urine.

Fusion of 3D models derived from optical and radar images

Thanks to satellite and airborne imagery, 3D reconstruction of earth surface is possible. Optical imagery exploits stereoscopic acquisitions and photogrammetry to retrieve 3D surface whereas interferometry is used for radar imagery. These techniques are complementary. Radar images allow the retrieval of fine metallic objects such as pylons. Optical imagery is more robust but such fine details cannot be preserved due to smoothing. An objective of the post-doctorate is to detect such fine objects.
The complementarity between 3D cloud points retrieved from satellite optical imagery and satellite and airborne radar imagery should lead to a 3D product including objects principally detected by radar and surface reconstruction derived from optical imagery.
The post-doctorate will begin with a state of the art review on 3D reconstruction by optical and radar imagery as well as cloud points fusion. Different 3D reconstruction processing chains should be used on airborne and satellite images. A precise registration algorithm and fusion algorithm on cloud points should be developed, enabling the detection of points detected only by radar. For this step, Deep Learning techniques could be useful. The results will be compared to 3D very high resolution acquired by Lidar to quantify the results quality of the proposed algorithm.
This post-doctorate will take place in labs specialized in satellite and radar image processing through a collaboration between CEA-DAM and Onera.

simulation of bi-metallic componants by 3D printing

This position will take place in the frame of MADE 3D European project. The objective of this is to build and run numerical simulations of bi-material L-PBF process taking account both thermal and mechanical behaviors. ANSYS© software will be used for this work. Numerical results will be compared to experimental results from Dedicated samples wich will be designed and manufactured by the post-doctoral student.

Earthquake effect on underground facilities

The Industrial Centre for Geological Disposal (Cigeo) is a project for a deep geological disposal facility for radioactive waste to be built in France. These wastes will be put in sealed packages in tunnels designed at 500 meters depth. The seals are made of a bentonite/sand mixture which has a high swelling capacity and a low water permeability. As a part of the long-term safety demonstration of the repository, it must be demonstrated that the sealing structures can fulfill their functions under seismic loads over their entire lifetime. In order to guarantee this future nuclear waste repository, CEA and Andra are collaborating to work on the potential scientific and engineering challenges involved.
The responses of underground repository to earthquake events are complex due to the spatially and temporally evolving hydro-mechanical properties of the surrounding media and the structure itself. Accurate modeling of the behavior, therefore, requires a coupled multiphysics numerical code to efficiently model the seismic responses for these underground repositories within their estimated lifespan of 100 thousand years.
The research will therefore, propose a performance assessment for sequential and parallel finite element numerical modeling for earthquake analysis of deep underground facilities. Then perform a synthetic data sampling to account for material uncertainties and based on the obtained results in the previous assessment, run a sensitivity analysis using a FEM or a metamodeling process. Finally, the results and knowledge gained within the span of this project will be processed and interpreted to provide responses for industrial needs.

Deep learning methods with Bayesian-based uncertainty quantification for the emulation of CPU-expensive numerical simulators

In the context of uncertainty propagation in numerical simulations, substitute mathematical models, called metamodels or emulators are used to replace a physico-numerical model by a statistical (or machine) learning model. This metamodel is trained on a set of available simulations of the model and mainly relies on machine learning (ML) algorithms. Among the usual ML methods, Gaussian process (GP) metamodels have attracted much interest since they propose both a prediction and an uncertainty for the output, which is very appealing in a context of safety studies or risk assessments. However, these GP metamodels have limitations, especially in the case of very irregular models. The objective of the post-doctorate will be to study the applicability and potential of Bayesian-based deep learning approaches to overcome these limitations. The work will be focused on Bayesian neural networks and deep GP and will consist in studying their tractability on medium size samples, evaluate their benefit compared to shallow GP, and assess the reliability of the uncertainty associated with their predictions.