Identification versus anonymisation from an embedded client operating on a blockchain
The first worldwide deployment of a blockchain dates back to 2010 with Bitcoin, which introduced a completely digital monetary system and a crypto-currency, bitcoin. Within Bitcoin, all transactions are publicly accessible and traceable, which should generate trust between stakeholders. However, the traceability of transactions, and ultimately of the crypto-currency, does not imply the traceability of users authenticated by an account address, or more precisely by a set of account addresses that are independent of each other. In this context, it can be complex to trace the individuals or legal entities owning the crypto-currency.
Crypto-currency is not the only use case supported by blockchain technology. The deployment of Ethereum in 2014, based on the use of smart contracts, opened up many other uses, in particular the protection of identifying data. In this area, the need for traceability versus furtivity can vary greatly from one use case to another. For example, on a blockchain that records the access of a worker owning an employment certificate to an industrial site, no information enabling the worker to be identified or his activity to be traced should appear. On the other hand, in the case of data collected by IoT sensors and processed by remote Edge devices, traceability of data and processing is desirable.
The thesis proposes to study different techniques for tracing digital assets on a blockchain, for stealthing their owners, and offering the possibility of auditing and identification by an authorised body. The aim is to build embedded devices, Edge or personal possibly embedding artificial intelligence, secured by hardware components, integrating different cryptographic solutions and account, data or identity wallet structures to meet the needs of the different use cases envisaged.
Biogas upgrading with an advanced Biorefinery for CO2 conversion
The use of renewable energy sources is a major challenge for the coming decades. One way of meeting the growing demand for energy is to valorize waste. Among the various strategies currently developed, the recovery of biogas from anaerobic digestion plants appears to be a promising approach. Biogas is composed mainly of methane, but also of unused CO2 (around 40%). The project proposed here is to reform biogas using a renewable biohydrogen source to convert the remaining CO2 into pure CH4. We propose to set up a stand-alone advanced biorefinery that will combine photoproduction of hydrogen from waste (e.g.: lactoserum) by the bacterium Rhodobacter capsulatus combined with the CO2 present in the biogas in a biomethanation unit containing a culture of Methanococcus maripaludis, a methanogenic archaea able to produce CH4 from CO2 and H2 only (according to the Sabatier reaction). The aim is to produce CH4 in an energy-efficient and environmentally-friendly way.
Self Forming Barrier Materials for Advanced BEOL Interconnects
Context : As semiconductor technology scales down to 10 nm and below, Back End of Line (BEOL) scaling presents challenges, particularly in maintaining the integrity of copper interconnects, where line/via resistance and copper fill are key issues. Copper (Cu) interconnections must resist diffusion and delamination while maintaining optimal conductivity. In the traditional Cu damascene process, metal barriers and a Cu seed layer are deposited by PVD to enable electrochemical copper deposition. As dimensions shrink, it becomes increasingly difficult to incorporate tantalum-based diffusion barriers, even with techniques like atomic layer deposition (ALD), as the barrier thickness must be reduced to just a few nanometers. To address this challenge, a self-forming barrier (SFB) process has been proposed. This process uses copper alloys containing elements such as Mn, Ti, Al, and Mg, which segregate at the Cu-dielectric interface, forming an ultra-thin barrier while also serving as a seed layer for electroplating.
Thesis Project: The PhD candidate will join a leading research team to explore and optimize materials for SFBs using Cu alloys. Focus areas include:
- Material Selection & Characterization: develop and analyze Cu alloy thin films by electrochemical and PVD methods to study their microstructure and morphology.
- Barrier Formation: Control alloy migration at the Cu/dielectric interface during thermal annealing and assess barrier effectiveness.
- Electrical & Mechanical Properties: Evaluate SFB impact on electrical resistance, electromigration, and delamination, especially in accelerated tests.
Required skills : Master's degree in electrochemistry or materials science with a strong interest in applied research. A pronounced interest in experimental work, skills in thin film deposition, electrochemistry and materials characterization (AFM, SEM, XPS, XRD, SIMS). You should be able to conduct bibliographic research and organize your work efficiently.
Work Environment: The candidate will work in a renowned laboratory with state-of-the-art 200/300 mm facilities and will participate in the CEA’s NextGen Project on advanced interconnects for high reliability applications.
Advanced functions for monitoring power transistors (towards greater reliability and increased lifespan of power converters for energy)
In order to increase the power of electronic systems, a common approach is to parallelize components within modules. However, this parallelization is complicated by the dispersion of transistor parameters, both initial and post-aging. Fast switching of Wide Bandgap (WBG) semiconductors components often requires slowdowns to avoid over-oscillation and destruction.
An intelligent driving scheme, including adjusted control, control of internal parameters of circuits and devices, as well as a feedback loop, could improve reliability, service life and reduce the risk of breakage.
The objectives of the thesis will be to develop, study and analyze the performance of control and piloting functions of power components, in silicon carbide (SiC) or gallium nitride (GaN), which could ultimately be implemented in a dedicated integrated circuit (ASIC type).
This thesis subject aims to solve critical problems in the parallelization of power components, thus contributing to eco-innovation by increasing the lifespan of power modules.
The design of integrated circuits requires, at the end of the chain, circuit editing and failure analysis tools. One of these tools is the probing of electrical potential levels using an electron beam available in a SEM (Scanning Electron Microscope) to determine the electrical signal present in an area of the circuit, which may be a metal level or a transistor. This electronic probing technique was widely used in the 90s, and then partially abandoned despite a few recurrent publications on the technique. In recent years, this technique has been revived by using the backside of the component, probing via the silicon substrate and accessing the active areas of the component.
These debugging and failure analysis tools are also tools for attacking integrated circuits. This thesis topic falls within the scope of hardware cybersecurity and so-called invasive attacks. The PhD student will implement this electron beam probing technique on commercial SEMs and under conditions of use specific to cybersecurity. Various techniques will be considered to improve the probed signals and their use.
Development of a microfluidic bioanalytical platform to quantify the cellular bio-distribution of a drug
A drug's mode of action and efficacy are correlated not only with its ability to accumulate in the targeted pathological tissues, i.e. its tissue bio-distribution, but also with its ability to specifically reach its molecular target within cells. Non-specific accumulation of a drug in these cells can be the cause of undesired effects, such as side effects during chemotherapy. In other words, assessing a drug's efficacy, specificity and absence of toxicity requires precise, quantitative determination of its cellular bio-distribution. Antibody-drug conjugates (ADCs) have become an indispensable tool in oncology, enabling vectorized therapy to preferentially target a subset of tumor cells expressing the antigen recognized by the antibody.
These ADCs target specific tumor cells expressing a particular antigen, thus limiting toxicity to healthy tissue. Radioactive labeling of drugs (3H, 14C) is a key method for quantifying their accumulation in tumor and non-tumor cells, in order to assess targeting accuracy and avoid undesirable side effects. However, the detection of low-level tritium emissions requires new technological solutions. The project proposes the development of an innovative microfluidic platform to detect and quantify these isotopes in single cells. This approach will enable us to better document ADC distribution in heterogeneous tissues and refine therapeutic strategies.
A revolution in intervention in complex environments: AI and Digital twins in synergy for innovative and effective solutions.
Scientific Context
The operation of complex equipment, particularly in the nuclear sector, relies on quick and secure access to heterogeneous data. Advances in generative AI, combined with Digital Twins (DT), offer innovative solutions to enhance human-system interactions. However, integrating these technologies into critical environments requires tailored approaches to ensure intuitiveness, security, and efficiency.
Proposed Work
This thesis aims to develop a generative AI architecture enriched with domain-specific data and accessible via mixed reality, enabling a glovebox operator to ask natural language questions. The proposed work includes:
A review of the state-of-the-art on Retrieval-Augmented Generation (RAG), ASR/TTS technologies, and Digital Twins.
The development and integration of a chatbot for nuclear operations.
The evaluation of human-AI interactions and the definition of efficiency and adoption metrics.
Expected Outcomes
The project aims to enhance safety and productivity through optimized interactions and to propose guidelines for the adoption of such systems in critical environments.
Complex 3D structuring based on DNA origami
The rapid evolution of new technologies, such as autonomous cars and renewable energy, requires the development of increasingly complex structures. To achieve this, many surface patterning techniques are available today. In microelectronics, optical lithography is the standard method for creating micro- and nanometric patterns. However, it remains limited in terms of the diversity of shapes it can produce.
In recent years, a promising approach has been developed within the laboratories of CBS (INSERM in Montpellier) and the CEA Leti (Grenoble): DNA origami assembly. This technology exploits the self-assembly properties of the DNA origami polymer chain. The assembly of nanometric DNA origami ultimately forms micrometric structures. The aim of this PhD is to explore new perspectives by combining 2D and 3D origami to create novel structures. These patterns could be of great interest for applications in fields such as optics or energy.
Predicting thermodynamic properties of defects in medium-entropy alloys from the atomic scale through statistical learning
The properties and behaviour of materials under extreme conditions are essential for energy systems such as fission and fusion reactors. However, accurately predicting the properties of materials at high temperatures remains a challenge. Direct measurements of these properties are limited by experimental instrumentation, and atomic-scale simulations based on empirical force fields are often unreliable due to a lack of precision.This problem can be solved using statistical learning techniques, which have recently seen their use explode in materials science. Force fields constructed by machine learning achieve the degree of accuracy of {it ab initio} calculations; however, their implementation in sampling methods is limited by high computational costs, generally several orders of magnitude higher than those of traditional force fields. To overcome this limitation, two objectives will be pursued in this thesis: (i) to improve active statistical learning force fields by finding a better accuracy-efficiency trade-off and (ii) to create accelerated free energy and kinetic path sampling methods to facilitate the use of computationally expensive statistical learning force fields.
For the first objective, we improve the construction of statistical learning force fields by focusing on three key factors: the database, the local atomic environment descriptor and the regression model. For the second objective, we will implement a fast and robust Bayesian sampling scheme to estimate the anharmonic free energy, which is crucial for understanding the effects of temperature on crystalline solids, using an adaptive bias force method that significantly improves convergence speed and overall accuracy.We will apply the methods developed to the calculation of free energy and its derivatives, physical quantities that give access to the thermo-elastic properties of alloys and the thermodynamic properties of point defects. To do this, we will use algorithmic extensions that allow us to sample a specific metastable state and also the transition paths to other energy basins, and thus to estimate the free energies of formation and migration of vacancy defects. The thermodynamic quantities calculated will then be used as input data for kinetic Monte Carlo methods, which will make it possible to measure the diffusion coefficients in complex alloys as a function of temperature.
One aim will be to try to relate the atomic transport properties to the complexity of the alloy. Since our approach is considerably faster than standard methods, we will be able to apply it to complex alloys comprising the elements W, Ti, V, Mo and Ta at temperatures and compositions that have not been studied experimentally.
Design of asynchronous algorithms for solving the neutron transport equation on massively parallel and heterogeneous architectures
This PhD thesis work aims at designing an efficient solver for the solution to the neutron transport equation in Cartesian and hexagonal geometries for heterogeneous and massively parallel architectures. This goal can be achieved with the design of optimal algorithms with parallel and asynchronous programming models.
The industrial framework for this work is in solving the Boltzmann equation associated to the transportof neutrons in a nuclear reactor core. At present, more and more modern simulation codes employ an upwind discontinuous Galerkin finite element scheme for Cartesian and hexagonal meshes of the required domain.This work extends previous research which have been carried out recently to explore the solving step ondistributed computing architectures which we have not yet tackled in our context. It will require the cou-pling of algorithmic and numerical strategies along with programming model which allows an asynchronousparallelism framework to solve the transport equation efficiently.
This research work will be part of the numerical simulation of nuclear reactors. These multiphysics computations are very expensive as they require time-dependent neutron transport calculations for the severe power excursions for instance. The strategy proposed in this research endeavour will decrease thecomputational burden and time for a given accuracy, and coupled to a massively parallel and asynchronousmodel, may define an efficient neutronic solver for multiphysics applications.
Through this PhD research work, the candidate will be able to apply for research vacancies in highperformance numerical simulation for complex physical problems.