AI Enhanced MBSE framework for joint safety and security analysis of critical systems
Critical systems must simultaneously meet the requirements of both Safety (preventing unintentional failures that could lead to damage) and Security (protecting against malicious attacks). Traditionally, these two areas are treated separately, whereas they are interdependent: An attack (Security) can trigger a failure (Safety), and a functional flaw can be exploited as an attack vector.
MBSE approaches enable rigorous system modeling, but they don't always capture the explicit links between Safety [1] and Security [2]; risk analyses are manual, time-consuming and error-prone. The complexity of modern systems makes it necessary to automate the evaluation of Safety-Security trade-offs.
Joint safety/security MBSE modeling has been widely addressed in several research works such as [3], [4] and [5]. The scientific challenge of this thesis is to use AI to automate and improve the quality of analyses. What type of AI should we use for each analysis step? How can we detect conflicts between safety and security requirements? What are the criteria for assessing the contribution of AI to joint safety/security analysis?
CORTEX: Container Orchestration for Real-Time, Embedded/edge, miXed-critical applications
This PhD proposal will develop a container orchestration scheme for real-time applications, deployed on a continuum of heterogeneous computing resources in the embedded-edge-cloud space, with a specific focus on applications that require real-time guarantees.
Applications, from autonomous vehicles, environment monitoring, or industrial automation, applications traditionally require high predictability with real-time guarantees, but they increasingly ask for more runtime flexibility as well as a minimization of their overall environmental footprint.
For these applications, a novel adaptive runtime strategy is required that can optimize dynamically at runtime the deployment of software payloads on hardware nodes, with a mixed-critical objective that combines real-time guarantees with the minimization of the environmental footprint.
3D interconnects for the design and fabrication of quantum processor units
To increase the performance of quantum computers, three-dimensional (3D) integration is now the key! Using technologies such as flip-chip bonding, multi-layer wiring or even through-silicon vias (TSV), 3D integration offers solutions to increase the number of qubits on a processor, reduce signal loss and cross-talk and even improve thermal management. All of these aspects are essential to continue scaling qubits to achieve fault-tolerant quantum computing.
Our team is developing 3D interconnect technologies (e.g. superconducting microbumps and TSV) for the next generation of quantum processors. This thesis will focus on the electrical and radiofrequency characterization of such interconnects and of the quantum devices integrated nearby to gain knowledge on how these 3D technological bricks may impact the quantum properties.
This position will bring you at the boundary between material, technological and physical challenges of quantum systems. You will work with teams from CEA-LETI and CEA-IRIG. As a PhD candidate, you will take part in the design and layout of test vehicles and in their fabrication. You will also lead the low temperature measurements of the fabricated samples, perform the associated analysis and write reports.
A theoretical framework for the task-based optimal design of Modular and Reconfigurable Serial Robots for rapid deployment
The innovations that gave rise to industrial robots date back to the sixties and seventies. They have enabled a massive deployment of industrial robots that transformed factory floors, at least in industrial sectors such as car manufacturing and other mass production lines.
However, such robots do not fit the requirements of other interesting applications that appeared and developed in fields such as in laboratory research, space robotics, medical robotics, automation in inspection and maintenance, agricultural robotics, service robotics and, of course, humanoids. A small number of these sectors have seen large-scale deployment and commercialization of robotic systems, with most others advancing slowly and incrementally to that goal.
This begs the following question: is it due to unsuitable hardware (insufficient physical capabilities to generate the required motions and forces); software capabilities (control systems, perception, decision support, learning, etc.); or a lack of new design paradigms capable to meet the needs of these applications (agile and scalable custom-design approaches)?
The unprecedented explosion of data science, machine learning and AI in all areas of science, technology and society may be seen as a compelling solution, and a radical transformation is taking shape (or is anticipated), with the promise of empowering the next generations of robots with AI (both predictive and generative). Therefore, research can tend to pay increasing attention to the software aspects (learning, decision support, coding etc.); perhaps to the detriment of more advanced physical capabilities (hardware) and new concepts (design paradigms). It is however clear that the cognitive aspects of robotics, including learning, control and decision support, are useful if and only if suitable physical embodiments are available to meet the needs of the various tasks that can be robotized, hence requiring adapted design methodologies and hardware.
The aim of this thesis is thus to focus on design paradigms and hardware, and in particular on the optimal design of rapidly-produced serial robots based on given families of standardized « modules » whose layout will be optimized according to the requirements of the tasks that cannot be performed by the industrial robots available on the market. The ambition is to answer the question of whether and how a paradigm shift may be possible for the design of robots, from being fixed-catalogue to rapidly available bespoke type.
The successful candidate will enrol at the « Ecole Doctorale Mathématiques, STIC » of Nantes Université (ED-MASTIC), and he or she will be hosted for three years in the CEA-LIST Interactive Robotics Unit under supervision of Dr Farzam Ranjbaran. Professors Yannick Aoustin (Nantes) and Clément Gosselin (Laval) will provide academic guidance and joint supervision for a successful completion of the thesis.
A follow-up to this thesis is strongly considered in the form of a one-year Post-Doctoral fellowship to which the candidate will be able to apply, upon successful completion of all the requirements of the PhD Degree. This Post-Doctoral fellowship will be hosted at the « Centre de recherche en robotique, vision et intelligence machine (CeRVIM) », Université Laval, Québec, Canada.
Artificial Intelligence for the Modeling and Topographic Analysis of Electronic Chips
The inspection of wafer surfaces is critical in microelectronics to detect defects affecting chip quality. Traditional methods, based on physical models, are limited in accuracy and computational efficiency. This thesis proposes using artificial intelligence (AI) to characterize and model wafer topography, leveraging optical interferometry techniques and advanced AI models.
The goal is to develop AI algorithms capable of predicting topographical defects (erosion, dishing) with high precision, using architectures such as convolutional neural networks (CNN), generative models, or hybrid approaches. The work will include optimizing models for fast inference and robust generalization while reducing manufacturing costs.
This project aligns with efforts to improve microfabrication processes, with potential applications in the semiconductor industry. The expected results will contribute to a better understanding of surface defects and the optimization of production processes.
Differential phase contrast imaging based on quad-pixel image sensor
Biopharmaceutical production is booming and consists of using cells to produce molecules of interest. To achieve this, monitoring the culture and the state of the cells is essential. Quantitative phase imaging by holography is a label-free optical method that has already demonstrated its ability to measure the concentration and viability of cultured cells. However, implementing this technique in a bioreactor faces several challenges related to the high cell density. It is therefore necessary to develop new quantitative phase imaging methods, such as differential phase contrast imaging.
The objective of the PhD is to develop this technique using a specific image sensor for which a prototype has been designed at CEA-LETI. The PhD candidate will use this new sensor and develop the reconstruction and image-processing algorithms. They will also identify the limitations of the current prototype and define the specifications for a second prototype that will be developed at CEA-LETI. Finally, they will consider the design of an inline probe to be immersed in the bioreactor.
Task planning under constraints
The autonomy of embedded systems, in particular robots, comes from their capability to plan their next actions. Although, it becomes critical to ensure the safety of the behaviour as the systems are exposed to human interaction more and more (eg. Autonomous cars, toy UAVs, cobots in manufacturing and so on).
The goal of the thesis is to study task planning under constraints: select the best sequence of actions while optimising several criteria like efficiency, safety and other domain specifics. The thesis involves two main axes, the first is to study how to model the systems constraints in a manner that can be understood both by the human experts and the planning algorithm (eg. Using Operational Design Domain or Dynamic Assurance Case to evaluate system’s safety). Ontologies and knowledge graphs would probably be adequate to model the constraints. The model would benefit from their expressivity and the already-existing tooling. The second main axis is the improvement of the planning algorithm to leverage those models. Those models shall have a generic structure since it is necessary to represent many natures of constraints: safety, efficiency/cost, social “confort”, shared resources on the critical path, type and quantity of interactions between the agents, geometric feasibility, ...
As the thesis is aimed at robotic autonomous systems, it will be important to demonstrate and evaluate the system on real-world use cases.
Design of an integrated circuit for decoding motor brain activity for autonomous use of a brain-machine interface for motor substitution
This work is part of the development of brain-machine interfaces dedicated to restoring mobility for patients with severe chronic motor disabilities. The proposed technological solutions are based on decoding brain signals acquired at the motor cortex level in order to extract movement intentions. These intentions serve as commands for motor compensation systems. Our team is a pioneer in this field, having developed WIMAGINE, one of the first chronic wireless implants, as well as a decoder and effectors adapted to the needs of paraplegic or quadriplegic patients (Benabid et al, The Lancet Neurology, 2019 ; Lorach et al, Nature 2023).
The proposed research follows on from an initial thesis whose objective was to design an integrated circuit capable of replicating the performance of the brain signal decoder with extremely low energy consumption, using a fixed model. However, due to changes in the user's strategy or the natural evolution of their brain structures, the performance of the decoding model tends to deteriorate over time, requiring regular recalibration. Initial strategies to compensate for these phenomena have been identified. The candidate's objective will be to refine these strategies and propose an implementation in the form of a low-power digital circuit.
The thesis will be carried out in Grenoble, within a dynamic project team composed of recognized experts in the design and clinical validation of brain-machine interfaces. The team is particularly distinguished in the design of specific integrated circuits and the development of signal decoding algorithms. This framework will allow the doctoral student to evolve in a stimulating scientific environment and to promote their research work, both in France and abroad.
Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment
Power converters are essential in numerous applications such as industry, photovoltaic systems, electric vehicles, and data centers. Their conventional maintenance is often based on fixed schedules, leading to premature replacement of components and significant electronic waste.
This PhD project aims to develop a novel non-invasive and low-cost ultrasound-based monitoring approach to assess the state of health and remaining useful life (RUL) of power converters deployed across various industries.
The research will focus on identifying and characterizing ultrasonic signatures emitted by aging electronic components, and on developing physics-informed neural networks (PINNs) to model their degradation mechanisms. The project will combine experimental studies with advanced signal processing and AI techniques (compressed sensing), aiming to detect early signs of failure and enable predictive maintenance strategies executed locally (edge deployment).
The research will be carried out within a Marie Sklodowska-Curie Actions (MSCA) Doctoral Network, offering international training, interdisciplinary collaboration, and secondments at leading academic and industrial partners across Europe (Italy and Netherlands for this PhD offer).
On-line monitoring of bioproduction processes using 3D holographic imaging
The culture of adherent is a promising approach for various bioproduction applications, such as drug manufacturing and delivery, regenerative medicine, and tracking of cellular differentiation. However, the analysis of single cell morphology and behavior without affecting the substrate integrity remains a major challenge. Lens-free holographic imaging is emerging as a promising solution for real-time, non-invasive monitoring of cellular processes. This technique captures wide field of view images without requiring exogenous labeling or sample manipulation, thus preserving the integrity of the cellular environment.
This thesis proposes the development of a 3D lens-free imaging system to monitor adherents cells in near real-time. The microscope will be coupled with advanced algorithms for data reconstruction and analysis and tested on different cell models. The use of deep learning techniques will allow for real-time segmentation and analysis of single cells, facilitating the tracking of cellular dynamics. This innovative project paves the way to a non-invasive monitoring of 3D multicellular samples, with potential applications on organ-on-chip and more complex organoids systems.