Enabling efficient federated learning and fine-tuning for heterogeneous and resource-constrained devices
The goal of this PhD thesis is to develop methods that enhance resource efficiency in federated learning (FL), with particular attention to the constraints and heterogeneity of client resources. The work will first focus on the classical client-server FL architecture, before extending the investigation to decentralised FL settings. The proposed methods will be studied in the context of both federated model training and distributed fine-tuning of large models, such as large language models (LLMs).
Development of an online measurement method for radioactive gases based on porous scintillators
As the national metrology laboratory for ionizing radiation, the Henri Becquerel National Laboratory (LNE-LNHB) of the French Alternative Energies and Atomic Energy Commission (CEA) operates unique facilities dedicated to radionuclide metrology. These include various setups for producing liquid-phase standards, as well as systems for mixing radioactive gases. In previous research projects, a specific installation was developed for the generation of radioactive gas atmospheres [1], with the aim of creating new testing and calibration methods that meet the needs of both research and industry.
One of the major current challenges is to reproduce environmental conditions as realistically as possible in order to better address actual regulatory requirements—particularly regarding volumetric activity and measurement conditions. This general issue applies to all radioactive substances, but is especially critical for volatile radioactive substances. Over the past several years, through numerous projects and collaborations, CEA/LNHB has been exploring new detection methods that outperform traditional liquid scintillation techniques. Among these innovations are new porous inorganic scintillators [1], which enable not only online detection but also online separation (“unmixing”) of pure beta-emitting radionuclides—this technique has been patented [2].
The objective of this PhD project is to develop, implement, and optimize these measurement methods through applications to:
- Pure radioactive gases,
- Multicomponent mixtures of pure beta-emitting radioactive gases—using porous scintillators for unmixing and identification,
- Liquid scintillation counting, more generally, where this unmixing capability has recently been demonstrated at LNHB and is currently being prepared for publication.
The unmixing technique is of particular interest, as it significantly simplifies environmental monitoring by scintillation, especially in the case of ³H and ¹4C mixtures. Currently, such analyses require multiple bubbler samplings, mixing with scintillation cocktail, and triple-label methods—procedures that involve several months of calibration preparation and weeks of experimentation and processing.
This PhD will be closely aligned with a second doctoral project on Compton-TDCR [1] (2025–2028), aimed at determining the response curve of the scintillators.
The scientific challenges of the project are tied to radionuclide metrology and combine experimentation, instrumentation, and data analysis to develop innovative measurement techniques. Key objectives include:
- Developing a method for beta-emitter unmixing in scintillation, based on initial published and patented concepts.
- Assessing the precision of the unmixing method, including associated uncertainties and decision thresholds.
- Validating the unmixing technique using the laboratory’s radioactive gas test bench [1], with various radionuclides such as 3H, 14C, 133Xe, 85Kr, 222Rn,... or via conventional liquid scintillation counting.
- Enhancing the unmixing model, potentially through the use of machine learning or artificial intelligence tools, particularly for complex multicomponent mixtures.
Internalisation of external knowledge by foundation models
To perform an unknown task, a subject (human or robot) has to consult external information, which involves a cognitive cost. After several similar experiments, it masters the situation and can act automatically. The 1980s and 1990s saw explorations in AI using conceptual graphs and schemas, but their large-scale implementation was limited by the technology available at the time.
Today's neural models, including transformers and LLM/VLMs, learn universal representations through pre-training on huge amounts of data. They can be used with prompts to provide local context. Fine-tuning allows these models to be specialised for specific tasks.
RAG and GraphRAG methods can be used to exploit external knowledge, but their use for inference is resource-intensive. This thesis proposes a cognitivist approach in which the system undergoes continuous learning. It consults external sources during inference and uses this information to refine itself regularly, as it does during sleep. This method aims to improve performance and reduce resource consumption.
In humans, these processes are linked to the spatial organisation of the brain. The thesis will also study network architectures inspired by this organisation, with dedicated but interconnected “zones”, such as the vision-language and language models.
These concepts can be applied to the Astir and Ridder projects, which aim to exploit foundation models for software engineering in robotics and the development of generative AI methods for the safe control of robots.
New experimental constraints on the weak interaction coupling constants by coincidence measurements of complex decay schemes
Accurate experimental knowledge of forbidden non-unique beta transitions, which constitute about one third of all known beta transitions, is an important and very difficult subject. Only a few reliable studies exist in the literature. Indeed, the continuous energy spectrum of these transitions is difficult to measure precisely for various reasons that cumulate: high diffusivity of electrons in matter and non-linearity of the detection system, unavailability of some radionuclides and presence of impurities, long half-lives and complex decay schemes, etc. Accurate theoretical predictions are equally difficult because of the necessity of coupling different models for the atomic, the nuclear and the weak interaction parts in the same, full-relativistic formalism. However, improving our knowledge of forbidden non-unique beta transitions is essential in radioactivity metrology to define the becquerel SI unit in the case of pure beta emitters. This can have a strong impact in nuclear medicine, for the nuclear industry, and for some studies in fundamental physics such as dark matter detection and neutrino physics.
Our recent study, both theoretical and experimental, of the second forbidden non-unique transition in 99Tc decay has highlighted that forbidden non-unique transitions can be particularly sensitive to the effective values of the weak interaction coupling constants. The latter act as multiplicative factors of the nuclear matrix elements. The use of effective values compensates for the approximations used in the nuclear structure models, such as simplified correlations between the nucleons in the valence space, or the absence of core excitation. However, they can only be adjusted by comparing with a high-precision experimental spectrum. The predictability of the theoretical calculations, even the most precise currently available, is thus strongly questioned. While it has already been demonstrated that universal values cannot be fixed, effective values for each type of transition, or for a specific nuclear model, are possible. The aim of this thesis is therefore to establish new experimental constraints on the weak interaction coupling constants by precisely measuring the energy spectra of beta transitions. Ultimately, establishing robust average effective values of these coupling constants will be possible, and a real predictive power for theoretical calculations of beta decay will be obtained.
Most of the transitions of interest for constraining the coupling constants have energies greater than 1 MeV, occur in complex decay schemes and are associated to the emission of multiple gamma photons. In this situation, the best strategy consists in beta-gamma detection in coincidence. The usual detection techniques in nuclear physics are appropriate but they must be extremely well implemented and controlled. The doctoral student will rely on the results obtained in two previous theses. To minimize self-absorption of the electrons in the source, they will have to adapt a preparation technique of ultra-thin radioactive sources developed at LNHB to the important activities that will be required. He will have to implement a new apparatus, in a dedicated vacuum chamber, including a coincidence detection of two silicon detectors and two gamma detectors. Several studies will be necessary, mechanical and by Monte Carlo simulation, to optimize the geometric configuration with regard to the different constraints. The optimization of the electronics, acquisition, signal processing, data analysis, spectral deconvolution and the development of a complete and robust uncertainty budget will all be topics covered. These instrumental developments will make possible the measurement with great precision of the spectra from 36Cl, 59Fe, 87Rb, 141Ce, or 170Tm decays. This very comprehensive subject will allow the doctoral student to acquire instrumental and analytical skills that will open up many career opportunities. The candidate should have good knowledge of nuclear instrumentation, programming and Monte Carlo simulations, as well as a reasonable knowledge of nuclear disintegrations.
Development of ultra-high-resolution magnetic microcalorimeters for isotopic analysis of actinides by X-ray and gamma-ray spectrometry
The PhD project focuses on the development of ultra-high-resolution magnetic microcalorimeters (MMCs) to improve the isotopic analysis of actinides (uranium, plutonium) by X- and gamma-ray spectrometry around 100 keV. This type of analysis, which is essential for the nuclear fuel cycle and non-proliferation efforts, traditionally relies on HPGe detectors, whose limited energy resolution constrains measurement accuracy. To overcome these limitations, the project aims to employ cryogenic MMC detectors operating at temperatures below 100 mK, capable of achieving energy resolutions ten times better than that of HPGe detectors. The MMCs will be microfabricated at CNRS/C2N using superconducting and paramagnetic microstructures, and subsequently tested at LNHB. Once calibrated, they will be used to precisely measure the photon spectra of actinides in order to determine the fundamental atomic and nuclear parameters of the isotopes under study with high accuracy. The resulting data will enhance the nuclear and atomic databases used in deconvolution codes, thereby enabling more reliable and precise isotopic analysis of actinides.
In situ study of the impact of the electric field on the properties of chalcogenide materials
Chalcogenide materials (PCM, OTS, NL, TE, FESO, etc.) are the basis of the most innovative concepts in microelectronics, from PCM memories to the new neuromorphic and spinorbitronic devices (FESO, SOT-RAM, etc.). Part of their operation relies on out-of-equilibrium physics induced by the electronic excitation resulting from the application of an intense electric field. The aim of this thesis is to measure experimentally on chalcogenide thin films the effects induced by the intense electric field on the atomic structure and electronic properties of the material with femtosecond (fs) time resolution. The 'in-operando' conditions of the devices will be reproduced using a THz fs pulse to generate electric fields of the order of a few MV/cm. The induced changes will then be probed using various in situ diagnostic methods (optical spectroscopy or x-ray diffraction and/or ARPES). The results will be compared with ab initio simulations using a state-of-the-art method developed with the University of Liège. Ultimately, the ability to predict the response of different chalcogenide alloys on time scales fs under extreme field conditions will make it possible to optimise the composition and performance of the materials (e- switch effect, electromigration of species under field conditions, etc.), while providing an understanding of the underlying fundamental mechanisms linking electronic excitation, evolution and the properties of the chalcogenide alloys.
Fine-grained and spatio-temporally grounded large multimodal models
This PhD project focuses on enhancing Large Multimodal Models (LMMs) through the integration of fine-grained and spatio-temporal information into training datasets. While current LMMs such as CLIP and Flamingo show strong performance, they rely on noisy and coarse-grained image-text pairs and often lack spatial or temporal grounding. The thesis aims to develop automatic pipelines to enrich image datasets with geographic and temporal metadata, refine captions using fine-grained semantic descriptors, and balance dataset diversity and compactness by controlling class-wise sample sizes.
Training strategies will incorporate hierarchical class structures and adapt protocols to improve alignment between caption elements and image regions. The work will also explore joint training regimes that integrate fine-grained, spatial, and temporal dimensions, and propose set-based inference to improve the diversity of generated outputs. The enriched datasets and models will be evaluated using existing or newly developed benchmarks targeting contextual relevance and output diversity. The project also addresses challenges in metadata accuracy, efficient model adaptation, and benchmarking methodologies for multi-dimensional model evaluation.
Applications include improved synthetic data generation for autonomous driving, enhanced annotation of media archives through contextual captioning, and better visual reasoning in industrial simulation scenarios.
In-depth electrical and material characterization of low-K spacer
As part of the European Chip Act, CEA-Leti is pioneering a new generation of transistors using FDSOI architecture. Our goal is to deliver advanced performance with a strong emphasis on materials and energy efficiency. As we push the limits of planar transistors at 10 nm and 7 nm, we face significant physical challenges, particularly in reducing parasitic elements like capacitance and access resistance, which are critical for minimizing energy loss and optimizing performance. We are eager to tackle these challenges together.
We are excited to offer a unique PhD opportunity for motivated students interested in the field of semiconductor device engineering. Join our team to work on the incorporation and characterization of low-k spacer for advanced 7-10nm FDSOI Technology. This PhD offers the chance to work on a groundbreaking project. If you're curious, innovative, and eager for a challenge, this opportunity is perfect for you!
The impact of the dielectric spacer nature has relevant effects on the overall transistor performances, specifically in non-fully overlapped configuration. The dielectric spacer integration, optimization and engineering remains a challenge and becomes crucial to address technology advancement and scaling down demand. Numerous spacer candidates (SiN, SiCO, SiCON, SiCBN) have been introduced and identified as promising solutions, however, they frequently suffer from inherent defects and adverse electrical characteristics, such as charge trapping and presence of undesired interface states, which hinder their and the overall transistors performance.
Within this framework, the objective of this PhD is to conduct a comprehensive investigation and electrical characterization (CV,IV, BTI, HCI…) of the material spacer (interface, volume), providing an in-depth analysis of transistor performance and its underlying mechanisms. Innovative ultrafast CV stress-measurement characterization on dielectric samples will be also carried out and the correlation between trapping performance and the deposition parameters used in their fabrication will be established. Additionally, the candidate will collaborate closely with experts to contribute to the thin film deposition and characterization of new materials through surface analyses and thin-film characterizations (ellipsometry, FTIR, XRR, XPS…)
Throughout this journey, you will gain a broad spectrum of knowledge, spanning microelectronics materials and processes, analog integrated design, all while addressing the unique challenge of advance 7-10 nm FDSOI technology. You'll collaborate with multidisciplinary teams to develop a deep understanding of FDSOI devices and analyze existing and new measurements. You'll also be part of an integrated multidisciplinary lab, working alongside a team composed of several permanent researchers, exploring a wide range of research applications.
Microfluidics for biomimetic detection of airbone pathogens
Air represents a complex contamination pathway that is difficult to control and through which numerous biological, biochemical, or chemical agents can affect populations and healthcare workers. Standard detection approaches, whether qPCR, antigen tests, or ELISA tests, rely on reagents specific to known and targeted agents. These approaches are therefore unsuitable for detecting an unknown pathogen that could result in a new pandemic. To face such unknown agents, new biosensors will be needed to distinguish between pathogenic and non-pathogenic agents. Also, these sensors will have to be miniature for deployment.
With a new microfluidic system the present project aims to explore original approaches for conducting such detection without preconceived notions. Based on the laboratory's experience and developments, the PhD will include :
- developing new materials and designs to optimize and to enable multiple bioaerosol sampling;
- developing a biomimetic biochip and optimize molecular interactions using microflows controlled at the micro/milliscale.
You will design a microfluidic card integrating new detection strategies and study them experimentally using prototypes already developed in the laboratory.
Simulation of interaction phenomena between ultrasonic waves and metallic microstructures for imaging and characterization
The interaction of waves with matter strongly depends on the frequency of these waves and on the scale of their wavelengths relative to the properties of the medium under consideration. In the context of ultrasonic imaging applications that are of interest to us, the relevant length scales for metals are generally on the order of millimeters (from tenths to several tens of millimeters). Depending on the manufacturing processes used, metallic media—often anisotropic—may also exhibit microstructures with heterogeneities of similar characteristic dimensions. As a result, ultrasonic waves propagating through metals can, under certain circumstances, be significantly affected by these microstructures. This may hinder some ultrasonic techniques (due to attenuation or structural noise), or conversely, offer an opportunity to estimate local properties of the inspected metal.
The general objective of the proposed PhD thesis is to deepen the understanding of the relationship between microstructure and ultrasonic wave behavior for broad classes of materials, leveraging the combined expertise of LEM3 in virtual microstructure generation and CEA in ultrasonic wave propagation simulation.
The proposed work will combine the acquisition and analysis of experimental data (both material and ultrasonic), the use of simulation tools, and statistical data processing. This will enable an analysis of wave behavior across material classes, and possibly the development of inversion procedures to characterize a microstructure based on ultrasonic datasets. The combination of these methods will support a holistic approach, contributing to significant advancements in the field.