Optimizing the estimation of the mass of the nuclear material by advanced statistical methods
In order to comply with safety and security standards for nuclear waste storage and non-proliferation treaties, producers of waste containing uranium or plutonium often need to measure the amount of nuclear materials in their radioactive waste. The radiological characterization of nuclear materials by passive and active neutron measurement is one of the historical research activities of the Nuclear Measurement Laboratory (LMN) of the CEA/IRESNE Institute.
Proportional counters filled with 3He or covered with boron are the reference detectors used for these techniques, which are reference tools for measuring plutonium or uranium. In passive measurement, neutron coincidence makes it possible to discriminate spontaneous fission events associated in particular with 240Pu from neutrons resulting from (a, n) reactions. In active measurement, the active neutron interrogation technique (DDT) provides information on the amount of fissile isotopes inside a waste package.
In order to reduce the sensitivity of neutron measurement techniques to matrix attenuation and contaminant localization effects, one of the objectives of the thesis is to study the coupling of different types of measurements, such as channel-by-channel measurement, emission tomography or high-energy X-ray radiography, within a framework of advanced statistical methods. The thesis also aims to evaluate the contribution of advanced statistical methods, such as regression algorithms, Bayesian approaches (among which the Gaussian process), and neural networks, to reduce the uncertainty associated with the plutonium mass.
Particular attention will be paid to the treatment of heterogeneities in the matrix and the distribution of the radioactive contaminant. The influence of these heterogeneities can be particularly difficult to quantify, requiring not only the use of advanced statistical methods, but also an in-depth experimental study using the SYMETRIC neutron measurement station of the CEA/IRESNE Institute.
The thesis work will be carried out at the CEA site of Cadarache Nuclear Measurement Laboratory, which is a professional laboratory, expert in non-destructive methods of radiological, elementary and physical characterization of objects whether radioactive or not. It is equipped with leading technological platforms, located in the TOTEM facility (neutron and gamma measurements) and the INB Chicade (SYMETRIC platforms for neutron measurement and CINPHONIE for high-energy RX imaging). Finally, the doctoral student will work in a collaborative environment where the different teams interact closely with each other.
Towards a high spatial resolution pixel detector for particle identification: new detectors contribution to physics
Future experiments on linear colliders (e+e-) with low hadronic background require improvements in the spatial resolution of pixel vertex detectors to the micron range, in order to determine precisely the primary and secondary vertices for particles with a high transverse momentum. This kind of detector is set closest to the interaction point. This will provide the opportunity to make precision lifetime measurements of short-lived charged particles. We need to develop pixels arrays with a pixel dimension below the micron squared. The proposed technologies (DOTPIX: Quantum Dot Pixels) should give a significant advance in particle tracking and vertexing. Although the principle of these new devices has been already been studied in IRFU (see reference), this doctoral work should focus on the study of real devices which should then be fabricated using nanotechnologies in collaboration with other Institutes. This should require the use of simulation codes and the fabrication of test structures. Applications outside basics physics are X ray imaging and optimum resolution sensors for visible light holographic cameras.
3D chemical analysis of downscaled ePCM devices for sub-18 nm technology nodes using STEM-EDX tomography and machine learning tools
The context of this PhD is the recent progress of Phase-Change Memory technology in the embedded applications (ePCM). The ultimate scaling of ePCM for sub-18nm nodes poses many challenges not only in fabrication, but also in the physico-chemical characterization of these devices. The aim of the project is to study the 3D chemical segregation/crystallization phenomena in new PCM alloys integrated into planar and vertical ePCM scaled devices, using electron tomography in STEM-EDX (and 4D-STEM) mode. Given the extreme downscaling and the complex geometry of the devices, the focus will be on optimizing experimental conditions and applying machine learning and deep learning techniques to improve the quality and reliability of the obtained 3D results. A correlation with the device electrical behavior will be carried out to better understand the phenomena behind failures after endurance and after data loss at high temperatures.
A probe-corrected Cold-FEG NeoARM TEM (60kV-200kV) will be used for the tomographic data acquisition. It is equipped with two large solid angle SSD detectors (JEOL Centurio), a CEOS Energy-Filtering and Imaging Device (CEFID) and a Timepix3 direct electron camera. The candidate will also have access to in-house Python codes as well as to the computing resources needed to carry out the spectral and tomographic data analysis.
Development of a ML-based analysis framework for fast characterization of nuclear waste containers by muon tomography
This PhD thesis focuses on developing an advanced analysis framework for inspecting nuclear waste containers using muon tomography, particularly the scattering method. Muon tomography, which leverages naturally occurring muons from cosmic rays to scan dense structures, has proven valuable in areas where traditional imaging methods fail. CEA/Irfu, with expertise in muon detectors, seeks to harness AI and Machine Learning (ML) to optimize muon data analysis, particularly to reduce long exposure times and improve image reliability.
The project will involve familiarizing with muography (muon tomography image) principles, simulating muon interactions with waste containers, and developing ML-based data augmentation and image processing techniques. The outcome should yield efficient tools to interpret muon images, enhance analysis speed, and classify container contents reliably. The thesis aims to improve nuclear waste inspection’s safety and reliability by producing cleaner, faster, and more interpretable muon tomography data through innovative analysis methods.
On-line monitoring of bioproduction processes using 3D holographic imaging
The culture of adherent cells on microcarriers (MCs) is a promising approach for various bioproduction applications, such as drug manufacturing and delivery, regenerative medicine, and tracking of cellular differentiation. However, the analysis of single cell morphology and behavior without affecting the substrate integrity remains a major challenge. Lens-free holographic imaging is emerging as a promising solution for real-time, non-invasive monitoring of cellular processes. This technique captures wide field of view images without requiring exogenous labeling or sample manipulation, thus preserving the integrity of the cellular environment.
This thesis proposes the development of a 3D lens-free imaging system to monitor cells on MCs in near real-time. The microscope will be coupled with advanced algorithms for data reconstruction and analysis in on-line bioreactors. The use of deep learning techniques will allow for real-time segmentation and analysis of single cells, facilitating the tracking of cellular dynamics. This innovative project paves the way to a non-invasive monitoring of 3D multicellular samples, with potential applications on organ-on-chip and more complex organoids systems.
Optimization by Artificial Intelligence of In Situ Characterization of Pure Beta Radionuclides in Complex Environments
Before, during, and after... the characterization of the radiological state is essential at all stages of the decommissioning scenario of a nuclear facility. Can we intervene directly on-site, or is teleoperation necessary? Has the contamination of a given area been completely eliminated? How should we categorize a particular nuclear waste to optimize its future management?
In-situ non-destructive nuclear measurements aim to evaluate the radiological state of processes and equipment in real time, while meeting criteria of efficiency, safety, flexibility, and reliability, and reducing costs through rapid, precise, and non-invasive analyses. While characterization techniques for gamma emitters are well mastered, those for pure beta emitters remain a significant challenge due to the low range of beta radiation in matter and the ambient gamma noise, which makes in-situ detection particularly complex.
The integration of artificial intelligence (AI) tools, such as machine learning or deep learning, in this field opens new perspectives. These technologies enable the automation of the analysis of large amounts of data while extracting complex information that is often difficult to interpret manually, particularly for deconvoluting continuous beta radiation spectra. Initial results obtained in the framework of L. Fleres' thesis have shown that AI can effectively predict and quantify the beta-emitting radionuclides present in a mixture. Although promising, this approach, tested in laboratory conditions, still needs to be qualified in real-world field conditions.
The proposed thesis aims to continue and refine these developments. It will involve integrating new algorithms, exploring various neural network architectures, and enriching learning databases to improve the performance of current systems for the in-situ characterization of beta emitters. This will include scenarios where the beta/gamma signal-to-noise ratio is degraded, as well as the detection of low levels of activity in the presence of natural radioactivity. Other research avenues will include the detection of low-energy radionuclides and the adaptation of deconvolution tools for large-surface detectors.
The characterization methodology developed at the end of the project will have strong potential for industrial valorization, particularly in the fields of decontamination and decommissioning. The doctoral candidate will join a team with extensive experience in the implementation of non-destructive radiological characterization techniques and methods in-situ and will have the opportunity to evaluate the proposed solutions on some of the largest decommissioning projects in the world.
Desired Profile: The ideal candidate holds a degree from an engineering school or a Master's (M2) with solid knowledge of nuclear measurement, particularly regarding the physical phenomena related to the interactions of ionizing radiation with matter. Skills in statistical data processing methods and programming (Python, C++) would also be appreciated.
Smart materials for low-carbon applications
The topic of this thesis focuses on the design of smart materials for low-carbon applications, with an emphasis on metallic additive manufacturing. This technology has revolutionized industrial production methods by enabling the creation of complex, lightweight parts while ensuring increased precision and flexibility. This is particularly relevant in demanding sectors such as aerospace, automotive, and nuclear industries, where reliability is crucial. By integrating optical sensors into metallic structures through additive manufacturing processes, it becomes possible to perform real-time monitoring of critical parameters such as stress, temperature, and radiation doses. This enhances the safety and efficiency of operational and maintenance activities. The thesis aims to address the challenges related to the monitoring and control of infrastructure conditions, ensuring continuous monitoring of structures and precise control of environmental parameters. Additionally, the study examines the durability of materials and how embedded sensors can function in hostile environments. Finally, this research aspires to develop solutions for effective and secure remediation and decommissioning processes.