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.
A formal framework for the specification and verification of distributed processes communication flows in clouds
Clouds are constituted of servers interconnected via the Internet, on which systems can be implemented, making use of applications and databases deployed on the servers. Cloud-based computing is gaining in popularity, and that includes the context of critical systems. As a result, it is useful to define formal frameworks for reasoning about cloud-based systems. One requirement about such a framework is that it enables reasoning about the concepts manipulated in a cloud, which naturally includes the ability to reason about distributed systems, composed of subsystems deployed on different machines and interacting through message passing to implement services. In this context, the ability to reason about communication flows is central. The aim of this thesis is to define a formal framework dedicated to the specification and verification of systems deployed on clouds. This framework will capitalize on the formal framework of "interactions". Interactions are models dedicated to the specification of communication flows between different actors in a system. The thesis work will study how to define structuring (enrichment, composition) and refinement operators to enable the implementation of classical software engineering processes based on interactions.
Fluctuations microscopy for functional imaging of organoids
Phase contrast microscopy and fluorescence microscopy are the two pillars of modern biological imaging. Phase contrast reveals the morphology of the sample, while fluorescent labeling provides specificity to the process of interest. In both cases, the image is the average value of the measured signal. In this thesis, we propose to focus not on the average value, but on the fluctuations observed in phase contrast. This new contrast will be called Fluctuations Imaging. The fluctuations arise from the active and passive transport phenomena that characterize cellular machinery, and it can be assumed that the level of fluctuations is correlated with cellular activity. The objective of the thesis is to detect phase contrast fluctuations, quantify them, and link them to a process of interest using machine learning methods. The object of study will be lymphocyte activation, which is a critical parameter for monitoring rejection in certain patients with type 1 diabetes who have undergone islet transplantation. Fluctuations Imaging would enable tracking without labeling, simplifying the monitoring protocol. The expected work is (i) optimizing a phase contrast microscope to detect fluctuations, (ii) analyzing image sequences to quantify them, and (iii) implementing the developed method on various biological models, some of which will be pancreas-on-a-chip organs. This thesis, at the intersection of instrumentation, biophysics, and biology, is intended for a student with a background in optics, physics, or equivalent, with a good knowledge of image processing and a strong interest in applications in biology and health.
Development of injectable adhesive hydrogels for the treatment of retinal tears
Retinal tears then detachment, a serious eye condition (20–25 cases per 100,000 in France each year), requires urgent surgery. Current treatments involve removing the vitreous, using gas as a tamponade agent, and sealing tears with laser. However, this method presents drawbacks, including patient restrictions (e.g., prolonged lying down) and complications (e.g., cataracts). Injectable hydrogels are being explored as alternative tamponade agents, but they do not display adhesive properties to suture the tears and laser treatment is still required. Surgical glues have also been tested, but cyanoacrylate-based adhesives are toxic, fibrin-based sealants are hard to use in the eye, and current hyaluronan (HA)-based materials lack sufficient stability and adhesion.
This PhD project aims to develop a sterile, injectable HA-based hydrogel with strong adhesive properties to seal retinal tears. Key requirements include biocompatibility, injectability (30G needle), tissue adhesiveness (1.5–3.7 N), and rapid delivery (within 1 hour). Our group has previously developed an injectable HA hydrogel with dynamic crosslinking, offering long-term stability, biocompatibility, and optical transparency. To confer it with tissue-adhesion properties, two strategies will be tested: (1) addition of tissue-adhesive tannic acid in the hydrogel formulation, or (2) grafting tissue-adhesive groups onto the HA backbone. The hydrogel will be tested for its biocompatibility and adhesiveness in preclinical eye models.
This innovative hydrogel could simplify retinal surgery, reduce complications, lower costs, and improve recovery. Beyond retinal repair, it may have applications in cornea surgery and other medical fields.
Implementation of TFHE on RISC-V based embedded systems
Fully Homomorphic Encryption (FHE) is a technology that allows computations to be performed directly on encrypted data, meaning that we can process information without ever knowing its actual content. For example, it could enable online searches where the server never sees what you are looking for, or AI inference tasks on private data that remain fully confidential. Despite its potential, current FHE implementations remain computationally intensive and require substantial processing power, typically relying on high-end CPUs or GPUs with significant energy consumption. In particular, the bootstrapping operation represents a major performance bottleneck that prevents large-scale adoption. Existing CPU-based FHE implementations can take over 20 seconds on standard x86 architectures, while custom ASIC solutions, although faster, are prohibitively expensive, often exceeding 150 mm² in silicon area. This PhD project aims to accelerate the TFHE scheme, a more lightweight and efficient variant of FHE. The objective is to design and prototype innovative implementations of TFHE on RISC-V–based systems, targeting a significant reduction in bootstrapping latency. The research will explore synergies between hardware acceleration techniques developed for post-quantum cryptography and those applicable to TFHE, as well as tightly coupled acceleration approaches between RISC-V cores and dedicated accelerators. Finally, the project will investigate the potential for integrating a fully homomorphic computation domain directly within the processor’s instruction set architecture (ISA).
Learning to focus: Physics-Informed Deep Learning for Super-Resolved Ultrasonic Phased-Array Imaging
This PhD aims to develop a new class of ultrasonic focusing methods for phased-array imaging by combining deep learning, physics-based modeling, and optimal transport theory. The first research axis introduces a reweighted, probabilistic extension of the Total Focusing Method (TFM), where per-isochrone focusing weights are iteratively estimated by a shared convolutional network and normalized using a neural time-of-flight field. This iterative, differentiable framework enables more adaptive, interpretable, and robust imaging in heterogeneous or uncertain media.
The second axis proposes a full reformulation of TFM as a Wasserstein barycenter problem, in which each partial image is modeled as an empirical distribution in a joint space of spatial coordinates and ultrasonic amplitude. A physically meaningful transport cost, based on geodesic distances that minimize time-of-flight variations with respect to selected emitters, encodes the acoustic geometry directly in the metric. The resulting grid-free barycenters yield sharp, physically consistent reflector localization and open new opportunities at the interface between optimal transport and ultrasonic phased-array imaging. Overall, the thesis aims to merge physics, machine learning, and geometric optimal transport to formulate next-generation reconstruction methods for ultrasonic imaging.