New High-Temperature Heat Pump (> 250°C) upgrading industrial waste heat < 90°C by hybridizing thermodynamic cycles
Decarbonizing industry is crucial to mitigate the impacts of human activities on the environment. Particularly, high-temperature heat (> 250°C) needs are substantial and difficult to decarbonize because of the high amount of energy required. Thermodynamic cycles of Heat Pump (HP), such as Reversed Brayton (RB) cycle, allow for upgrading industrial waste heat to higher temperature usable by processes. However, the energy efficiency of HP to produce heat > 250°C from 100°C from 80°C waste heat with low energy consumption was developed in our laboratory.
This doctoral thesis offers to study an innovative high-temperature HP combining an AHT with a RB cycle. The concept consists of using the AHT to elevate the temperature level of waste heat to temperature > 100°C and provide it to the RB cycle to generate high-temperature heat > 250°C at a low energy cost. Thus, this hybrid system offers a solution to meet industrial needs in decarbonized high-temperature heat. The main challenges laying ahead are the selection of relevant coupling architectures, as well as the study and development of efficient thermal transfer interfaces.
This research work will require mastering modelling tools of thermodynamic cycles and heat exchangers. The thesis will also involve an experimental aspect, since validation of the numerical results on a laboratory test bench are expected.
Fe–Zeolite coupling in a fixed-bed catalytic reactor for the direct hydrogenation of CO2 into sustainable aviation fuel (SAF)
The thesis aims to maximize CO2 conversion yields towards Sustainable Aviation Fuels (SAF, C8–C16). Currently, direct CO2 hydrogenation over an iron-based catalyst predominantly produces short-chain olefins (C2–C4), which do not meet SAF specifications. The proposed approach is to combine an iron-based catalyst with a zeolite catalyst within a single reactor, thereby integrating olefin production via CO2 hydrogenation with their subsequent conversion into longer-chain hydrocarbons in the SAF range (C8–C16). The zeolites investigated will exhibit properties tailored to this objective while retaining conventional formulations compatible with process scale-up.
The objective of the thesis is to understand how the formation of long-chain hydrocarbons suitable for the SAF fraction can be promoted by combining iron-based and zeolite catalysts within a single reactor. Two research areas will be investigated to achieve this objective. The first will focus on short olefin oligomerization (ethylene, propylene, butylene), assessing the influence of operating conditions and the presence of by-products from CO2 hydrogenation on the distribution of the resulting hydrocarbons. The second will investigate the integrated CO2 hydrogenation and olefin oligomerization process, aiming to understand the interactions between the two reactions and identify operating conditions that maximize selectivity and yield towards SAF-range hydrocarbons. Particular attention will be paid to the effects of thermal gradients and heat transfer phenomena on catalytic performance, product distribution, and catalyst stability.
Study of oxide formation at the interface of Grade 91/Grade 91 steels during a hot isostatic pressing (HIP)-assisted diffusion bonding cycle
This PhD project aims to understand and mitigate oxide formation at the interface of Grade 91 steels joined by hot isostatic pressing (HIP)-assisted diffusion bonding.
This process offers significant potential for manufacturing complex components for the future DEMO nuclear fusion reactor.
However, the formation of oxides, particularly silica (SiO2), at the bonding interface can weaken the joints and significantly reduce their impact toughness.
The main objective is to identify the physicochemical parameters governing oxide formation at high temperatures.
The study will focus on the effects of silicon and manganese contents, temperature, and oxygen partial pressure.
Model alloys and Grade 91 steel will be investigated experimentally under controlled atmospheres.
Experimental findings will be compared with thermodynamic simulations based on the CALPHAD method.
HIP-bonded joints will then undergo multiscale microstructural characterization and mechanical testing.
Optimization strategies, including surface preparation and degassing conditions, will be assessed to minimize interfacial oxidation.
The expected outcomes include a reliable bonding procedure that can be transferred to EUROFER 97 steel and potentially applied to other industrial sectors.
Evaluation of new low-damage GaN etching strategies for the realization of high performance MicroLED
For optical communication and display applications (display, AR/VR), the need for micro-displays with high resolution µLEDs GaN is becoming increasingly important. The formation of the pixel constitutes the critical brick because it generates defects on the sides of the µLED, which limit the emission performance of the device. These degradations have even more impacts on electro-optical performance for aggressive LED pitches.
The subject proposes a comprehensive approach, ranging from the development of new GaN etching strategies limiting damage to the pixel edges, to its characterization through dedicated direct means, and validated on a complete device by an electro-optical test vehicle.
Backpropagation-Free Langevin Sampling for Bayesian Learning on ReRAM Architectures
Bayesian neural networks allow artificial intelligence systems to account for uncertainty in their predictions, but training them requires substantial computational resources. Resistive memories (ReRAM), which can both store data and perform computations directly within electronic circuits, offer a promising solution to reduce this cost.
This PhD project aims to develop a new Bayesian learning method on ReRAM hardware, based on Langevin sampling. This approach efficiently explores neural network parameters by combining random perturbations with gradient estimates. The objective is to exploit the intrinsic physical variability of ReRAM devices to estimate these gradients without relying on conventional backpropagation. The possibility of reusing memory programming attempts will also be investigated to reduce energy consumption and device wear.
The work will combine algorithm development, simulations, and experiments on existing ReRAM circuits at CEA-Leti. Expected outcomes include a demonstration of Bayesian learning on real hardware and an assessment of its performance, accuracy, and energy efficiency compared with existing approaches.
Bayesian Calibration of Multiphysics Models and Inverse Design of Neuromorphic Devices Assisted by Machine Learning
Electrochemical neuromorphic devices, such as ionic synaptic transistors, offer promising opportunities for the development of energy-efficient, brain-inspired computing architectures. However, their design and optimisation remain challenging due to the complex coupling between ion transport, electrochemical processes and electrostatics. Current approaches largely rely on iterative fabrication and experimental characterisation, limiting the exploration of the design space and the identification of optimal device configurations.
This PhD project aims to develop a predictive, physics-informed digital-twin framework to accelerate the modelling and inverse design of electrochemical neuromorphic devices. The proposed approach combines multiphysics modelling, Bayesian inference and machine learning to predict device behaviour, quantify uncertainties and explore device architectures in silico, thereby reducing reliance on trial-and-error experimental approaches.
Starting from an existing Bayesian inference pipeline and neural-network-based surrogate model, the project will investigate advanced learning strategies to efficiently approximate the posterior probability distribution of physical and geometrical model parameters conditioned on experimental measurements and/or target device performances. Particular emphasis will be placed on physics-informed learning approaches combining governing PDE constraints with sparse, punctual experimental observations, inspired by Physics-Informed Neural Operators (PINO). Neural operators, conditional generative models (including diffusion models) and other state-of-the-art probabilistic learning strategies will be investigated to achieve accurate and computationally efficient posterior approximation, uncertainty quantification and inverse design.
Beyond electrochemical neuromorphic devices, the developed framework could be extended to a broader class of nonlinear dielectric systems, including ferroelectric and antiferroelectric devices. These systems involve strongly nonlinear constitutive relationships and complex coupled equations, often making conventional numerical modelling and parameter identification particularly challenging. They therefore represent a promising application for the proposed physics-informed Bayesian and machine-learning methodology.
Multiscale 3D Characterization of Nanodevices Using EDX Tomography and Electron Ptychography
The increasing complexity of nanodevices requires multiscale 3D characterization techniques spanning from the nanometer to the atomic scale. STEM-EDX tomography provides three-dimensional elemental mapping at the nanometer scale. Recent deep learning-based approaches have achieved near-isotropic spatial resolution of approximately 5 nm in specimens 60–100 nm thick. Electron ptychography offers complementary capabilities for atomic-resolution structural characterization. This PhD aims to develop a correlative methodology combining both techniques on the same specimen. STEM-EDX tomography will first be performed on relatively thick TEM lamellae. The specimens will subsequently be thinned to a target thickness below 30 nm for 4D-STEM acquisition. Multislice electron ptychography will provide depth-resolved structural information. Simulations will be used to optimize experimental conditions and acquisition parameters. EDX spectral processing and limited-angle tomographic reconstruction methods will be optimized. Ptychographic reconstruction algorithms will be evaluated in terms of resolution, robustness, and sensitivity to light elements. A major challenge will be the spatial registration of chemical and structural volumes before and after specimen thinning. Reconstruction accuracy and limitations will be assessed using simulations and reference specimens. The methodology will be applied to phase-change memory (PCM) devices and InGaN-based nano-emitters. The ultimate goal is to establish a correlative 3D characterization approach linking chemical composition, atomic structure, and nanodevice properties.
Cryogenic exploration of 1/f and Random Telegraph noise effects in advanced transistors used in Quantum System control/readout Electronics
With the CMOS transistor downscaling, low-frequency noise (LFN) or “1/f noise” due to charge trapping/ detrapping in gate dielectric interface defects has become increasingly important, as its amplitude is inversely proportional to the gate area, thus limiting the performance of mainly analog and mixed signal circuits. For nano-scale transistors, another type of fluctuation appears, called "Random Telegraph Noise" (RTN), due to the activity of only a handful of traps. RTN consists of sudden, high-amplitude transitions of the current, which can jeopardize the stability of memory cells and the precision of imager circuits. Counterintuitively, not only both LFN and RTN present at deep cryogenic conditions, but in certain cases even more prevalent than at room temperature[4]. This is an enormous obstacle for quantum computers, because cryo-CMOS circuits need to be physically very close to the qubit devices in order to perform the tasks of control, readout, and signal amplification. While technological advancements could potentially improve function below 10 K and accurate IC design could mitigate the read/write or readout amplification issues, a significant effort on the physical understanding of the trapping/detrapping cryogenic phenomena and a precise mathematical description is required, not only for noise but also for aging effects such as Bias Temperature Instabilities (BTI).
This thesis aims to provide a deep understanding of trap-related noise effects at cryogenic temperatures down to 4 K through thorough experimental studies, while also developing physics-based compact noise models for cryo-circuit design. Recent interpretation approaches will be evaluated, and new methods of noise parameter extraction will be elaborated. In terms of technologies, studied devices will be FD-SOI MOSFETs 28nm node from ST and used for Q-LOOP project (IRT - UE project.
Experimental and theoretical approach to select deep eutectic solvents for the delamination of photovoltaic panels
Multimaterial assemblies recycling such as photovoltaic (PV) panels is necessary to guarantee the circularity of materials. Today, PV panels recycling is mostly managed through pyrometallurgy which is limited in terms of materials recovery (polymers are incinerated and recovered silicium and silver yields are low) and emits noxious and greenhouse gases, such as HF generated by fluorinated polymer incineration.
Chemical delamination processes consist in the immersion of panels into a hot solvent to swell the polymeric tie-layers and separate the materials. These processes have the potential to largely improve recovery yields of all materials, but they are environmentally impacting due to the operating conditions and solvent use.
The solvent choice defines the impact of the process. In the current literature, this choice is mostly based on trial and error approaches that are tedious. Moreover, efficient solvents for PV delamination are generally toxic and flammable, which limits the environmental relevance of the processes.
Deep eutectic solvents (DES), which are the association of a hydrogen bond donor and acceptor (HBD and HBA), present interesting solvent properties for such processes. The use of organic hydrophobic molecules as HBA or HBD can impart the solvents with a dual hydrophobic and hydrophilic character that brings a capacity to interact with various molecules to the solvent, including polymers. As HBA/HBD combinations are numerous, it is important to develop a systematic methodology for their choice.
The present topic proposes a methodology based on a combination of theoretical modelling and experimental validation measurements to better understand the contributions of HBD/HBA structures on DES properties. The study will focus on the identification of the interactions between DES and EVA (ethylene vinyl acetate), a widely used polymer in PV panels, through the study of phenomenon of solubilization and solvolysis (covalent bond breakage) to identify the contributions to the delamination phenomenon. At the end of the thesis, the DES selection will be validated through the demonstration of the delamination of a PV panel and the analysis of the separated layers.
Design and implementation of innovative alloys for high-performance NdFeB magnets via the grain-boundary diffusion process
Permanent magnets based on rare earth elements (RE), particularly neodymium-iron-boron (Nd-Fe-B) magnets, are essential for high-performance motors and generators for use in electric vehicles and wind turbines. To increase their resistance to demagnetization, heavy rare earth elements (HRE) such as dysprosium (Dy) or terbium (Tb) are partially substituted for neodymium. However, these HREs, although crucial for magnetic properties, are critical materials with supply risks. Hence, the challenge is to reduce their content while maintaining performance, in order to meet the growing demand for high-performance magnets. In this context, the grain boundary diffusion (GBD) process of HREs emerges as the most effective solution. It enables the diffusion of HREs from the surface of sintered magnets toward grain boundaries and then into the magnetic phase grains. This localization of HREs at the grain peripheries improves resistance to demagnetization while preserving high remanence, thereby reducing the required amount of HREs.
The objective of this Ph.D is to provide a comprehensive understanding of the diffusion and localization mechanisms of HREs while limiting parasitic reactions that unnecessarily consume them. The doctoral work will combine a comprehensive experimental approach, including the development of HRE-RE-M (where M is a transition metal) alloys for GBD, the study of diffusion, the characterization of equilibrium phases, and detailed magnetic and thermodynamic analyses, particularly through an in-depth study of phase diagrams. This approach aims to define the optimal compositions and diffusion conditions to achieve the targeted magnetic performances.