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.
Evaluation of nanoscale surface coatings on high energy density positive electrodes for lithium-ion batteries.
Nickel-rich layered oxides LiNi1-x-yMnxCoyO2 (NMC) and LiNi1-x-yCoyAlzO2 (NCA) are exceptional materials for the positive electrode of lithium batteries due to their high reversible storage capacity. However, under real conditions, undesired reactions can lead to the dissolution of transition metals and electrodes cracking, thus affecting their electrochemical properties. This phenomenon is linked to the presence of hydrofluoric acid (HF) in the electrolyte, mainly due to the degradation of the LiPF6 salt. To address this problem, surface treatments are needed to protect the active material and improve performance. The EVEREST project proposes an innovative, flexible, and affordable method for creating inorganic coatings at the nanoscale. This method is based on a recent technique, coaxial electrospinning, which allows the production of nanofibers with a well-defined core-sheath structure. For this project, we propose to evaluate the impact of nanofiber shaping parameters on morphology, electrochemical performance and the underlying mechanism. The electrochemical performances of the coated and the pristine positive electrodes will be compared in a half-cell with Li metal as a counter electrode. Redox processes, charge transfer mechanisms and structural modifications will be studied in the operando mode using the synchrotron radiation beam.
Quantum simulation of atomic nulei
Atomic nuclei constitute strongly correlated quantum many-body systems governed by the strong interaction of QCD. The nuclear shell model, which diagonalizes the Hamiltonian in a basis whose dimension grows exponentially with the number of nucleons, represents a well-established approach for describing their structure. However, this combinatorial explosion confines classical high-performance computing to a restricted fraction of the nuclear chart.
Quantum computers offer a promising alternative through their natural ability to manipulate exponentially large Hilbert spaces. Although we remain in the NISQ era with its noisy qubits, they could revolutionize shell model applications.
This thesis aims to develop a comprehensive approach for quantum simulation of complex nuclear systems. A crucial first milestone involves creating a software interface that integrates nuclear structure data (nucleonic orbitals, nuclear interactions) with quantum computing platforms, thereby facilitating future applications in nuclear physics.
The project explores two classes of algorithms: variational and non-variational approaches. For the former, the expressivity of quantum ansätze will be systematically analyzed, particularly in the context of symmetry breaking and restoration. Variational Quantum Eigensolvers (VQE), especially promising for Hamiltonian-based systems, will be implemented with emphasis on the ADAPT-VQE technique tailored to the nuclear many-body problem.
A major challenge lies in accessing excited states, which are as crucial as the ground state in nuclear structure, while VQE primarily focuses on the latter. The thesis will therefore develop quantum algorithms dedicated to excited states, testing various methods: Hilbert space expansion (Quantum Krylov), response function techniques (quantum equations of motion), and phase estimation-based methods. The ultimate objective is to identify the most suitable approaches in terms of scalability and noise resilience for applications with realistic nuclear Hamiltonians.
Modeling and prediction of electromagnetic emissions from power converters using deep learning
In recent years, electromagnetic compatibility (EMC) in power converters based on wide bandgap (WBG) semiconductors has attracted growing interest, due to the high switching speeds and increased frequencies they enable. While these devices improve power density and system efficiency, they also generate more complex conducted and radiated emissions that are challenging to control. In this context, this thesis focuses on the prediction, modeling, and characterization of electromagnetic interference (EMI) (> 30 MHz), both conducted and radiated, in high-frequency power electronic systems. The work is based on a multi-subsystem partitioning method and an iterative co-simulation approach, combined with in situ characterization to capture non-ideal and nonlinear phenomena. In addition, deep learning techniques are employed to model EMI behavior using both measured and simulated data. Generative artificial intelligence (Generative AI) is also leveraged to automatically generate representative and diverse configurations commonly encountered in power electronics, thereby enabling efficient exploration of a wide range of EMI scenarios. This hybrid approach aims to enhance analysis accuracy while accelerating simulation and design phases.
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.
Reducing the complexity of France's building stock to better anticipate anticipate energy demand flexibility and the integration of solar solar resources
The aim of this work is to respond to the current challenges of energy transition in the building sector, France's leading energy consumer. French public policies are currently proposing far-reaching solutions, such as support for energy-efficient home renovation and incentives for the installation of renewable energy production systems. On a large scale, this is leading to structural changes for both building managers and energy network operators. As a result, players in the sector need to review their energy consumption and carbon impact forecasts, integrating flexibility solutions adapted to the French standard. Some flexibility levers are already in place to meet the challenges of energy and greenhouse gas emission reduction, but others need to be anticipated, taking into account long-term scenarios for energy renovation and the deployment of renewable energy sources, particularly photovoltaic energy, across the whole of France. The issue of massification is therefore an underlying one. That's why this thesis proposes to implement a methodology for reducing the size of the French installed base based on previously defined criteria. In particular, the aim will be to define a limited number of reference buildings that are statistically representative of the behavior resulting from the application of flexibility strategies that meet the challenges of energy efficiency and limiting greenhouse gas emissions. To this end, the CSTB (Centre Scientifique et Technique du Bâtiment) is developing and making available a database of French buildings (BDNB: Base de Données Nationale des Bâtiments), containing information on morphology, uses, construction principles and energy consumption and performance.
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.