Development of a new atomic reference database for radioactive processes

Several scientific communities have highlighted the lack of precision and the inconsistencies present in the reference atomic database EADL. The data were calculated using a fairly simple Dirac-Hartree-Slater approach and then subsequently corrected empirically. However, to date it remains the only database that is sufficiently complete to be usable by simulation codes. In recent years, a collaboration was initiated and reinforced during two successive European projects between the FCT-UNL (Lisbon, Portugal), the IPCMS (Strasbourg, France) and the LNHB (CEA Saclay, France). A new relativistic atomic code, based on the density functional theory, has been developed and validated by studying different electron capture transition probabilities. The aim of the present subject is to develop a new reference atomic database based on this atomic code. The required theoretical equations will have to be established. Several elements will be calculated and the predictions will be compared to available results in the literature. The influence of this precise atomic modeling on the atomic exchange effect that occurs in beta transitions will also be studied. At least one publication and one participation to an international conference are expected.

Causal learning

As part of a project that concerns the creation of innovative materials, we wish to strengthen our platform in its ability to learn from little experimental data.

In particular, we wish to work firstly on the extraction of causal links between manufacturing parameters and properties. Causality extraction is a subject of great importance in AI today and we wish to adapt existing approaches to experimental data and their particularities in order to select the variables of interest. Secondly, we will focus on these causal links and their characterization (causal inference) using an approach based on fuzzy rules, that is to say we will create fuzzy rules adapted to their representation.

Calibration of the high dose rate flash therapy beam monitor of the IRAMIS facility

Ultra-flash beams are pulsed beams of high-energy electrons (over a hundred MeV) with pulse durations in the femto-second range. The IRAMIS facility (CEA Saclay) uses laser acceleration to produce this type of beam, with a view to their application in radiotherapy. The LNHB is in charge of establishing dosimetric traceability for the IRAMIS facility, and to do this it has to calibrate the facility's monitor. Current radiotherapy facilities are based on medical linear accelerators operating at energies of up to 18 MeV in electron mode. LNHB has such equipment. It is used to establish national references in terms of absorbed dose to water, under the conditions of the IAEA protocol TRS 398.
Establishing dosimetric traceability involves choosing the measurement conditions, knowing the transfer dosimeter characteristics used and any corrections to be applied to the measurements taking into account the differences between the IRAMIS Facility and those of LNHB.

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.

POST-DOC/CDD X-ray tomography reconstruction based on Deep-Learning methods

CEA-LIST is developing the CIVA software platform, a benchmark for the simulation of non-destructive testing processes. In particular, it offers tools for X-ray and tomographic inspection which, for a given inspection, can simulate all radiographies, taking into account various associated physical phenomena, as well as the corresponding tomographic reconstruction. CEA-LIST also has an experimental platform for robotized X-ray tomography inspection.
The proposed work is part of the laboratory's contribution to a bilateral French-German ANR project involving academic and industrial partners, focusing on the inspection of large-scale objects using the robotized platform. A sufficient number of X-rays must be taken in order to carry out a 3D reconstruction of the object. In many situations, some angles of view cannot be acquired due to the dimensions of the object and/or the motion limitations of the robots used, resulting in a loss of quality in the 3D reconstruction.
Expected contributions focus on the use of Deep-Learning methods, to complete missing projections on the one hand, and reduce reconstruction artifacts on the other. This work includes the CIVA-based steps of building a simulated database and evaluating the obtained results using POD (Probability Of Detection) measurements.
The candidate will have access to the facilities of the Paris Saclay research center and will be expected to promote his/her results in the form of scientific communications (international conferences, publications).
Candidate profile:
PhD in data processing or artificial intelligence.
Fluent English (oral presentations, scientific publications).
Previous knowledge of X-ray physics and tomographic reconstruction methods would be appreciated.

X-ray tomography reconstruction based on analytical methods and Deep-Learning

CEA-LIST develops the CIVA software platform, a reference for the simulation of non-destructive testing processes. In particular, it proposes tools for X-ray and tomographic inspection, which allow, for a given tomographic testing, to simulate all the radiographic projections (or sinogram) taking into account various associated physical phenomena, as well as the corresponding tomographic reconstruction.
The proposed work is part of the laboratory's contribution to a European project on tomographic testing of freight containers with inspection systems using high-energy sources. The spatial constraints of the projection acquisition stage (the trucks carrying the containers pass through an inspection gantry) imply an adaptation of the geometry of the source/detector system and consequently of the corresponding reconstruction algorithm. Moreover, the system can only generate a reduced number of projections, which makes the problem ill-posed in the context of inversion.
The expected contributions concern two distinct aspects of the reconstruction methodology from the acquired data. On the one hand, it is a question of adapting the analytical reconstruction methods to the specific acquisition geometry of this project, and on the other hand, to work on methods allowing to overcome the lack of information related to the limited number of radiographic projections. In this objective, supervised learning methods, more specifically by Deep-Learning, will be used both to complete the sinogram, and to reduce the reconstruction artifacts caused by the small number of projections available. A constraint of adequacy to the data and the acquisition system will also be introduced in order to generate physically coherent projections.

Development of a new spectrometer for the characterization of the radionuclide-based neutron sources

Since few years, the LNHB is developing a new instrument dedicated to the neutron spectrometry, called AQUASPEC. The experimental device consists of a polyethylene container that is equipped with a central channel accommodating the source and 12-measurement channels (in a spiral formation) around the source, into which detectors can be placed. The container is filled with water in order to moderate neutrons emitted from the source. Measurements have performed with 6Li-doped plastic scintillators, optimized for the simultaneous detection of fast neutrons, thermal neutrons and gamma rays through the signal processing based on pulse shape discrimination (PSD). The spectrum reconstruction is performed with an iterative ML-EM or MAP-EM algorithm, by unfolding experimental data through the detector's responses matrix calculated with MCNP6 code. The candidate will work in the general way on issues related to the neutron spectrometry in the laboratory: Contribution to the development and validation of the new spectrometer AQUASPEC; Participation to the sources measurements and working on aspects of neutron detection and signal processing, in particular issue of the discrimination of neutron/gamma based on the pulse shape discrimination technique (PSD); Usage of Monte Carlo simulation codes and algorithms to reconstruct initial neutron energy distribution; Investigation and integration of information related to neutron/gamma coincidence specific to the XBe type sources.

Eco-innovation of insulating materials by AI, for the design of a future cable that is long-lasting, resilient, bio-sourced and recyclable.

This topic is part of a larger upcoming project for the AI-powered creation of a new electrical cable for future nuclear power plants. The goal is to design cables with a much longer lifetime than existing cables in an eco-innovative approach.
The focus is on the cable insulation because it is the most critical component for the application and the most sensitive to aging. The current solution is based on adding additives (anti-rad and antioxidants) to the insulation to limit the effects of irradiation and delay aging as much as possible. However, there is another solution that has never been tested before: self-repairing materials.
The project to which this topic is attached aims to design and manufacture several test model of insulation specimens. With several test characterization protocols, in order to verify the gain in terms of reliability and resilience. The results obtained will begin to fill a future database for the AI platform Expressif, developed at CEA List, which will be used to design the future cable.

Contribution to the metrological traceability of emerging alpha-emitting radiopharmaceuticals in the framework of the european AlphaMet project (Metrology for Emerging Targeted Alpha Therapies)

The Laboratoire national Henri Becquerel (LNE-LNHB) at CEA/Saclay is the laboratory responsible for the french references in the field of ionizing radiation. The LNHB is involved in the european EPM AlphaMet (Metrology for Emerging Targeted Alpha Therapies) submitted under the Metrology support for Health call (2022) to provide metrological support for clinical and preclinical studies; it began in September 2023 for a total duration of three years. The project comprises four Work Packages (WP) targeting different issues, with WP1 in particular dedicated to activity metrology and nuclear data measurements for imaging and dosimetry. This project aims at to improve the metrological traceability of emerging alpha-emitting radiopharmaceuticals such as 211At, 212Pb/212Bi, 225Ac.
The candidate will participate in the various tasks defined as part of the European AlphaMet project in which the LNHB is involved. Radiation-matter simulations will be carried out to study the response of the laboratory's ionisation chambers in various situations concerning: (i) the evolution of the response during the in-growth of the ?-emitting progeny of 225Ac, (ii) the quantification of the influence of the 210At impurity in the case of the measurement of 211At, and (iii) the search for a long-lived radionuclide surrogate of 212Pb for the quality control of dose calibrators. The candidate will also be involved in setting up a new device aimed at improving the linearity of the measurement of half-life with an ionization chamber. During the post-doctoral stay at LNHB, the candidate will interact with the various partners in the AlphaMet project (activity metrology laboratories, hospitals, clinical study centres).
The initial duration of the post-doctorate is 12 months (renewable) at the Laboratoire National Henri Becquerel (CEA/Saclay). It is hoped to start in the first half of 2024.