Integrated waste treatment: design and optimisation of a multi-waste treatment scheme for a multi-purpose energy production
At the city scale, multiple waste streams such as household waste, compost, sewage sludge, yard waste, non-recyclable plastics, used oils, metals, glass, and others. All of these feedstocks exhibit variable seasonality and carbon content. Nowadays, the aforementioned streams are managed through recycling, and in some cases incineration or landfilling. Alternative treatment technologies, such as gasification, hydrothermal gasification, and anaerobic digestion, are being explored as potential pathways to improve the overall sustainability of waste management.
Existing scientific studies have largely focused on the conversion of individual waste types or on the application of a single technology to a specific waste stream, without accounting for regional integration, resource variability or systemic assessment. A city-scale analysis of waste streams could enable the identification of synergies between different waste types and the identification of optimal conversion pathways.
In this context, a key scientific challenge lies in the development of an integrated, multi-waste treatment framework capable of modelling, optimizing, and assessing a multi-waste, multi-product energy system at the city scale. The objective of this PhD project is to investigate waste treatment at the city scale, accounting for the seasonality of waste generation, waste stream composition, and local energy demand (heat, electricity, and gas). The work will consider local and European regulations (Waste Framework Directive, AGEC law, and RED III directive) as well as techno-economic and environmental aspects. The study will focus on one to three representative geographic areas and will establish a methodology that can be further applied to a broad range of territorial contexts.
Hydrogen transport and trapping in austenitic alloys coupling experiments and simulations.
Molecular hydrogen H2 is an alternative energy carrier to traditional fossil fuels, gas or oil. It meet the current energy and environmental challenges, i.e. the need to store greenhouse gases free energy produced by intermittent means such as wind turbines or solar panel. Nevertheless, its safe storage and transportation is one of the keys to its use. The containers or pipes that carry the hydrogen must be leaktight and maintain their integrity over time, for both economical and safety reasons. Understanding and predicting the behavior of hydrogen in container/pipeline alloys and the associated mechanical degradation – such as embrittlement – is therefore crucial for the development of the hydrogen industry. These issues are also generic to all alloys exposed to a source of hydrogen, in corrosion or in the metallurgical industries where the hydrogen simply comes from contact with water, or in the oil&gas industry where hydrogen comes from hydrogen sulphides present in hydrocarbons.
If many experimental works have identified hydrogen embrittlement as the origin of the degradation of alloys exposed to hydrogen, large gray areas still remain on the mechanisms at work due to experimental difficulties and the great variability of the observed phenomena. In addition, the transport and trapping of hydrogen prior to mechanical degradation are poorly known and poorly documented at the nanoscale.
The objective of the thesis is to explore the mechanisms of hydrogen trapping / transport in austenitic materials, as well as its distribution in volume, prior to cracking in order to be able to report and explain the experimental observations.
To achieve this objective, the thesis work will be dedicated to the study of pure nickel, a model system for the austenite phase. The study will be carried out in three stages: (i) thermodesorption measurements and (ii) atomic scale simulations using molecular dynamics, both feeding chemical kinetics modeling coupled with Fick's law at the mesoscopic scale.
AI model deployment using Hardware-Aware on-chip Fine Tuning
Emerging unconventional hardware technologies are essential for future Edge-AI applications, but they often suffer from variability, mismatches, and technology dispersion. These non-idealities can strongly reduce AI inference accuracy if no fine-tuning or calibration is applied. Traditional supervised fine-tuning is difficult to industrialize because it raises issues related to data confidentiality, service quality, software complexity, and hardware constraints.
This PhD project aims to develop hardware-algorithm co-design methods that avoid the need for fully supervised on-chip retraining. The main goal is to create task-agnostic, inference-level self-calibration strategies able to compensate hardware mismatches at the system level. The work will study existing adaptation methods, including weight-based, feature-based, output-based, and domain adaptation approaches.
The project will define a relevant Edge-AI application, develop a generic fine-tuning method, and validate it through low-level electrical simulations. If possible, the proposed algorithm may also be tested experimentally on a custom ASIC-based hardware setup.
Multiscale modeling of the magnetic response of heterogeneous material
The spectral dependence of the permeability of magnetic materials, whether in composite or dense materials, remains a complex issue due to the different scales of the phenomena involved. Approximate analytical models are often used to describe the frequency response of magnetic materials, particularly to improve their performance in areas such as power electronics. Recent results have shown that micro-magnetism codes can now predict the response of a system of coupled nanoparticles or a particle representing the volume of the materials in question. This thesis aims to use these tools to improve existing analytical models. An inclusion immersed in an effective field will be the paradigm from which the domain structure and the spectral response of the particle will be calculated using a micro-magnetism code. The materials studied include spherical particles or those with a high aspect ratio (magnetic oxides, ferromagnetic petals) at varying concentrations, ranging from dilute media to dense materials. This work will identify pathways to optimize the microstructure of materials for better performance in applications such as power electronics and microwave components. To this end, CEA provides a scientific computing environment with access to HPC resources, as well as facilities for sample preparation and static and dynamic magnetic characterization. At the end of this work, the candidate will have gained a solid understanding of the microstructure-property relationships described by a numerical approach applied to magnetic materials. More generally, this approach is expanding in the field of materials to improve their properties in various fields, under the designation "materials by design".
Self-calibrated mmW Injection Locked Oscillators
In our research group we have developed and used during the last few year an innovative technique for frequency generation where the injection locked oscillator are the core. However, this circuits that we found in electronics, but also in other disciplines such as mechanics, biology and fundamental physics, hide still some secrets.
In this PhD you will be starting with the existing knowledge about these circuits leveraging in the broad experience of the research team and you will contribute to extend this knowledge to understand the impact of external perturbations and manufacturing tolerances on the operation of such circuit, for next proposing and implementing self-calibration and stabilization techniques.
The. applications of this circuits are broad, but some of the most appealing are found in the field of high-speed mmW wireless and wired links, extremely high resolution radar, vital signs detection for medical applications and quantum experiments (such as resonant paramagnetic spectrometry.
Depending on the advancement of the research the proposed self-calibration technique will be applied in one of the existing developments in our group in one of these fields.
Modeling the CSS growth of CsPbBr3
Lead-halide perovskites, particularly CsPbBr3, are emerging as promising materials for X-ray detection in medical applications. This technology requires their deposition in thick layers (>100 µm), and close-space sublimation (CSS), initially developed by CEA-Liten, has shown highly encouraging results. However, this process remains poorly understood at the microscopic scale, and the relationship between microstructure and performance remains a major scientific and industrial challenge.
This thesis, in partnership with the SIMAP laboratory, aims to develop a comprehensive thermodynamic model of the CSS process. The candidate will (i) experimentally generate the essential thermodynamic data for modeling, (ii) simulate growth mechanisms, and (iii) validate them experimentally using dedicated instrumented growth furnaces and advanced characterization techniques. Machine learning tools will be implemented to establish predictive correlations between deposition parameters and layer properties.
The results will enable optimization of CsPbBr3 growth for more sensitive and stable X-ray detectors, with a strong impact on medical imaging. This work will also provide opportunities for high-impact publications and patents in a highly competitive field.
CdTe for medical radiography; control of electrical properties
The use of direct-conversion detectors in medical radiography opens up new possibilities. Due to its properties, the semiconductor material CdTe has emerged as the material of choice for manufacturing these new components. The proposed thesis topic aims to develop the knowledge and processes necessary to produce CdTe crystals with properties tailored to specific application requirements. The work will draw on the laboratory’s advanced expertise in mastering CdTe single-crystal growth processes. The key challenges of the project will be as follows:
- Performing annealing under controlled atmospheres (ex-situ, on small samples) to study their impact on the electrical properties of CdTe,
- Conducting advanced characterizations to better understand the doping mechanisms in CdTe,
- Fabricating “simple” devices and testing them under X-ray flux to quantify the performance of the laboratory’s materials.
The proposed thesis topic is central to the development of a CdTe technology for medical radiography applications. Multidisciplinary work (material and process development, material characterization, fabrication and X-ray testing of simplified devices) is proposed to address this topic.
Energy-minimizing associative neural networks using resistive memories
This PhD project aims to develop Hopfield-type associative neural networks that perform inference through energy-minimizing dynamics.
The goal is to exploit these dynamics for image denoising and reconstruction close to the sensor, under strict energy and latency constraints.
The network synapses will be implemented in ReRAM crossbar arrays, enabling analog in-memory matrix-vector operations.
The work will focus on architecture dimensioning while accounting for array size, weight quantization, device variability and endurance limits.
Reference models will be developed in PyTorch to evaluate alternative neural dynamics and hardware mapping strategies.
Patch-wise image denoising will serve as the main use case to quantify trade-offs between reconstruction quality, latency and energy consumption.
Particular attention will be paid to the robustness of the networks against hardware non-idealities such as noise, variability and memory drift.
The project will also investigate local on-chip learning mechanisms, allowing slow adaptation to changes in the sensor, scene or memory devices.
These learning rules must remain compatible with the endurance constraints of resistive memories.
Ultimately, the PhD should provide hardware-sizing guidelines and support the design of an experimental test vehicle.
The broader scientific objective is to demonstrate that dynamic associative inference can become an efficient, robust and low-power building block for edge AI.
New methodologies for analyzing the impact of crystal defects on the electrical performance of SiC power devices
In our past studies on SiC power devices, the analysis of electrical performances on diodes [1] (idem for future MOSFETs) must take into account the impact of material's defects at the epitaxy and substrate level.
Initially, the thesis work will consist of setting up tools dedicated to our needs in the SiC team. The specifications for these tools have already been established as part of the internship currently underway within the LAPS laboratory. These AI tools will be able to be trained on already existing datasets (SiC diode batches: with electrical data, defect mappings) and complete the previous manually carried out analyses.
In a second phase, the use of the developed tools will be applied to new manufactured and characterized batches. The range of data will then be completed by considering new component architectures (diodes and power MOSFETs), new material characterizations (defects characterization from other tools being installed at Leti, or even with external collaborators: see Line Pilot WBG, see Soitec), new entries (images of defectivity, obtained during the components fabrication in the clean rooms).
Note that the approach applies i) in the case of power to other materials (GaN, diamond, Ga2O3...), ii) also potentially to any component on semiconductor (memory, transistor, photonic, quantum...).
Systemic validation of fuzzy rule bases: accounting for data availability and the specific characteristics of fuzzy inference
This PhD topic lies within the field of symbolic artificial intelligence. Unlike approaches based on neural networks, these methods rely on explicit rules, often provided by experts or learned from limited data, making them interpretable but potentially imperfect.
The central problem is therefore the validation of fuzzy rule bases: the goal is to ensure that the rules produce consistent, useful, and reliable results. Existing methods use global metrics (overall system performance) and local metrics (the quality of each rule), but they do not sufficiently account for certain important specificities. For example, interactions between rules can strongly influence the final behavior.
The thesis proposes to develop a comprehensive and systematic approach to validate these rule bases, whether data is available or not. In particular, it aims to design new metrics capable of capturing these interactions, drawing inspiration, for example, from graph-based approaches (such as FinGrams or reputation systems).
The work will include the definition of a methodological framework, the proposal of new validation measures, as well as their implementation and experimental evaluation.
The expected outcomes are more precise tools for detecting problematic rules, and an overall improvement in the performance and reliability of fuzzy inference systems.