Understanding the mechanisms of direct CO2 hydrogenation using (Na,K)FeOx catalysts via theoretical-experimental coupling
In the context of climate change, we need to reduce our CO2 emissions by using less energy. Another approach is to capture, store and use CO2, with the aim of moving towards a circular carbon economy and, ultimately, defossilization. With this in mind, the direct hydrogenation of CO2 enables it to be transformed into molecules of interest such as hydrocarbons, via the coupling of the reverse water gas shift (RWGS) reaction and Fischer-Tropsch synthesis (FTS).
Computational operando catalysis has recently emerged as a reasoned alternative to the development of new catalysts, thanks to a multi-scale approach from the atom down to the active particle, to model catalyst selectivity and activity. New tools combining ab initio simulations (DFT) and molecular dynamics (MD) via machine learning algorithms bridge the gap between the precision of DFT calculations and the power of atomistic simulations. Current bifunctional catalysts (active for RWGS, and FTS) for direct CO2 hydrogenation are based on doped iron oxides (metal promoters).
The aim of this project is the theoretical study of Na-FeOx and K-FeOx catalysts doped with Cu, Mn, Zn and Co, in 4 stages: DFT simulations (adsorption energies, density of states, energy barriers, transition states), microkinetic modeling (reaction constants, TOF), construction of interatomic potentials by DFT/machine learning coupling, simulation of whole particles (selectivity, activity, microscopic quantities).
This theoretical study will go hand in hand with the synthesis and experimental measurements of the studied catalysts, and optimized catalysts emerging from the computational results. All the accumulated data (DFT, MD, catalytic properties) will be fed into a database, which can eventually be exploited to identify descriptors of interest for CO2 hydrogenation.
Magneto-ionic gating of magnetic tunnel junctions for neuromorphic applications
Magneto-ionics is an emerging field that offers great potential for reducing power consumption in spintronics memory applications through non-volatile control of magnetic properties through gating. By combining the concept of voltage-controlled ionic motion from memristor technologies, typically used in neuromorphic applications, with spintronics, this field also provides a unique opportunity to create a new generation of neuromorphic functionalities based on spintronics devices.
The PhD will be an experimental research project focused on the implementation of magneto-ionic gating schemes in magnetic tunnel junction’s spintronics devices. The ultimate goal of the project is to obtain reliable and non-volatile gate-control over magnetisation switching in three-terminal magnetic tunnel junctions.
One major challenge remains ahead for the use of magneto-ionics in practical applications, its integration into magnetic tunnel junctions (MTJ), the building blocks of magnetic memory architectures. This will not only unlock the dynamic control of switching fields/currents in magnetic tunnel junctions to reduce power consumption, but also allow for the control of stochasticity, which has important implications in probabilistic computing.
Towards a better understanding of membrane proteins through AI
Despite the remarkable advances in artificial intelligence (AI), particularly with tools like AlphaFold, the prediction of membrane protein structures remains a major challenge in structural biology. These proteins, which represent 30% of the proteome and 60% of therapeutic targets, are still significantly underrepresented in the Protein Data Bank (PDB), with only 3% of their structures resolved. This rarity is due to the difficulty in maintaining their native state in an amphiphilic environment, which complicates their study, especially with classical structural techniques.
This PhD project aims to overcome these challenges by combining the predictive capabilities of AlphaFold with experimental small-angle scattering (SAXS/SANS) data obtained under physiological conditions. The study will focus on the translocator protein TSPO, a key marker in neuroimaging of several serious pathologies (cancers, neurodegenerative diseases) due to its strong affinity for various pharmacological ligands.
The work will involve predicting the structure of TSPO, both in the presence and absence of ligands, acquiring SAXS/SANS data of the TSPO/amphiphile complex, and refining the models using advanced modeling tools (MolPlay, Chai-1) and molecular dynamics simulations. By deepening the understanding of TSPO’s structure and function, this project could contribute to the design of new ligands for diagnostic and therapeutic purposes.
Design and fabrication of neuromorphic circuit based on lithium-iontronics devices
Neural Networks (NNs) are inspired by the brain’s computational and communication processes to efficiently address tasks such as data analytics, real time adaptive signal processing, and biological system modelling. However, hardware limitations are currently the primary obstacle to widespread adoption. To address this, a new type of circuit architecture called "neuromorphic circuit" is emerging. These circuits mimic neuron behaviour by incorporating high parallelism, adaptable connectivity, and in memory computation. Ion gated transistors have been extensively studied for their potential to function as artificial neurons and synapses. Even if these emerging devices exhibit excellent properties due to their ultra low power consumption and analog switching capabilities, they still need to be validated into larger systems.
At the RF and Energy Components Laboratory of CEA-Leti, we are developing new lithium-gated transistors as building blocks for deploying low-power artificial neural networks. After an initial optimization phase focused on materials and design, we are ready to accelerate the pace of development. These devices now need to be integrated into a real system to assess their actual performance and potential. In particular, both bio-inspired circuits and crossbar architectures for accelerated computation will be targeted.
During this 3-year PhD thesis, your (main) objective will be to design, implement, and test neural networks based on lithium-gated transistor crossbars (5x5, 10x10, 20x20) and neuromorphic circuits , along with the CMOS read and write logic to control them. The networks might be implemented using different algorithms and architectures, including Artificial Neural Network, Spiking Neural Networks and Recurrent Neural Networks, which will be then tested by solving spatial and/or temporal pattern recognition problems and reproduce biological functions such as pavlovian conditioning.
Embedded local blockchain on secure physical devices
The blockchain is based on a consensus protocol, the aim of which is to share and replicate ordered data between peers in a distributed network. The protocol stack, embedded in the network's peer devices, relies on a proof mechanism that certifies the timestamp and ensures a degree of fairness within the network.
The consensus protocols used in the blockchains deployed today are not suitable for embedded systems, as they require too many communication and/or computing resources for the proof. A number of research projects, such as IOTA and HashGraph, deal with this subject and will be analysed in the state of the art.
The aim of this thesis is to build a consensus protocol that is frugal in terms of communications and computing resources, and whose protocol stack will be implemented in a secure embedded device. This protocol must be based on the proof of elapsed time from our laboratory's work, which is also frugal, called Proof-of-Hardware-Time (PoHT), and must satisfy the properties of finality and fairness. The complete architecture of a peer node in the network will be designed and embedded on an electronic board including a microprocessor and several hardware security components, in such a way that the proof resource cannot be parallelized. Communication between peers will be established in a distributed manner.
Advancing Semantic Representation, Alignment, and Reasoning in Multi-Agent 6G Communication Systems
Semantic communications is an emerging and transformative research area, where the focus shifts from transmitting raw data to conveying meaningful information. While initial models and design solutions have laid foundational principles, they often rest on strong assumptions regarding the extraction, representation, and interpretation of semantic content. The advent of 6G networks introduces new challenges, particularly with the growing need for multi-agent systems where multiple AI-driven agents interact seamlessly.
In this context, the challenge of semantic alignment becomes critical. Existing literature on multi-agent semantic communications frequently assumes that all agents share a common understanding and interpretation framework, a condition rarely met in practical scenarios. Misaligned representations can lead to communication inefficiencies, loss of critical information, and misinterpretations.
This PhD research aims to advance the state-of-the-art by investigating the principles of semantic representation, alignment, and reasoning in multi-AI agent environments within 6G communication networks. The study will explore how agents can dynamically align their semantic models, ensuring consistent interpretation of messages while accounting for differences in context, objectives, and prior knowledge. By leveraging techniques from artificial intelligence, such as machine learning, ontology alignment, and multi-agent reasoning, the goal is to propose novel frameworks that enhance communication efficiency and effectiveness in multi-agent settings. This work will contribute to more adaptive, intelligent, and context-aware communication systems that are key to the evolution of 6G networks.
Study and simulation of phase entrainment in mixer-settler batteries
As part of the development of new liquid-liquid extraction separation processes, experimental tests are implemented to demonstrate the recovery of valuable elements sufficiently decontaminated from impurities. These tests are commonly carried out in mixer-settler batteries. However, depending on the operating conditions, these finished products may be contaminated by impurities. This contamination results from the combination of several factors:
-Hydrodynamic: Entrainment in the solvent of non-decanted aqueous drops containing impurities
-Chemical: the impurity separation factor is low (less than 10-3)
-Process: the entrainment of drops is amplified with the increase in the rate (reduction of the residence time of the drops)
This thesis aims to increase the understanding of the different phenomena responsible for these phase entrainments in order to estimate optimal operating parameters and to guarantee a contamination of the finished products below a fixed threshold. The aim will be to develop a macroscopic model to predict the flow rate of non-decanted droplets as a function of the operating conditions in the mixer-settler batteries. It will have to be based on hydrodynamic simulations coupling the resolution of a droplet population balance to a continuous phase flow. A coupling will be carried out between this hydrodynamic model and the PAREX or PAREX+ code to size the process diagrams. The qualification of the proposed models will have to be done by comparisons with experimental measurements (based on previous or future test campaigns).
Combined Software and Hardware Approaches for Large Scale Sparse Matrix Acceleration
Computational physics, artificial intelligence and graph analytics are important compute problems which depend on processing sparse matrices of huge dimensions. This PhD thesis focuses on the challenges related to efficiently processing such sparse matrices, by applying a systematic software are hardware approach.
Although the processing of sparse matrices has been studied from a purely software perspective for decades, in recent years many dedicated, and very specific hardware, accelerators for sparse data have been proposed. What is missing is a vision of how to properly exploit these accelerators, as well as standard hardware such as GPUs, to efficiently solve a full problem. Prior to solving a matrix problem, it is common to perform pre-processing of the matrix. This can include techniques to improve the numerical stability, to adjust the form of the matrix, and techniques to divide it into smaller sub-matrices (tiling) which can be distributed to processing cores. In the past, this pre-processing has assumed homogenous compute cores. New approaches are needed, to take advantage of heterogeneous cores which can include dedicated accelerators and GPUs. For example, it may make sense to dispatch the sparsest regions to specialized accelerators and to use GPUs for the denser regions, although this has yet to be shown. The purpose of this PhD thesis is to take a broad overview of the processing of sparse matrices and to analyze what software techniques are required to exploit existing and future accelerators. The candidate will build on an existing multi-core platform based on RISC-V cores and an open-source GPU to develop a full framework and will study which strategies are able to best exploit the available hardware.
Enhancing Communication Security Through Faster-than-Nyquist Transceiver Design
In light of the growing demand for transmission capacity in communication networks, it is essential to explore innovative techniques that enhance spectral efficiency while maintaining the reliability and security of transmission links. This project proposes a comprehensive theoretical modeling of Faster-Than-Nyquist (FTN) systems, accompanied by simulations and numerical analyses to evaluate their performance in various communication scenarios. The study will aim to identify the necessary trade-offs to maximize transmission rates while considering the constraints related to implementation complexity and transmission security, a crucial issue in an increasingly vulnerable environment to cyber threats. This work will help identify opportunities for capacity enhancement while highlighting the technological challenges and adjustments necessary for the widespread adoption of these systems for critical and secure links.
study of lithium isotopes separation by laser
This thesis concerns the study of different ways of separating lithium isotopes by laser. The work will be conducted both theoretically and experimentally. The objective is to determine an optimal way as well as its performances.