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.
Advancing Health Data Exploitation through Secure Collaborative Learning
Recently, deep learning has been successfully applied in numerous domains and is increasingly being integrated into healthcare and clinical research. The ability to combine diverse data sources such as genomics and imaging enhances medical decision-making. Access to large and heterogeneous datasets is essential for improving model quality and predictive accuracy. Federated learning is currently developed to support this requirement offering an alternative by enabling decentralized model training while ensuring that raw data remains stored locally at the client side. Several open-source frameworks integrate secure computation protocols for federated learning but remains limited in its applicability to healthcare and raises issues related to data sovereignty. In this context, a French framework is currently developed by the CEA-LIST, introduces an edge-to-cloud federated learning architecture that incorporates end-to-end encryption, including fully homomorphic encryption (FHE) and resilience against adversarial threats. Through this framework, this project aims to deliver modular and secure federated learning components that foster further innovation in healthcare AI.
This project will focus on three core themes:
1) Deployment, monitoring and optimization of deep learning models within federated and decentralized learning solutions.
2) Integrating large models in collaborative learning.
3) Developing aggregation methods for non-IID situation.
Optical intradermal sensing via instrumented microneedles
Cortisol plays a central role in regulating the circadian cycle and in many essential physiological processes such as energy metabolism and immune response. Conventional monitoring of cortisol relies on single blood or saliva samples, which do not accurately reflect the temporal dynamics of its secretion. It is therefore necessary to develop innovative approaches that enable continuous, minimally invasive, and reliable measurement of cortisol concentration in patients.
The doctoral project aims to develop an original optical instrumentation system coupled with microneedles functionalized with fluorescent aptamers for continuous, minimally invasive intradermal monitoring of cortisol without the need for sampling. Within this framework, the PhD candidate will be responsible for designing and sizing the future optical microneedles intended for cortisol detection. They will set up the experimental systems required to characterize the optical microneedles fabricated within the department and test their performance in a representative environment. Finally, the PhD candidate will develop a comprehensive data processing and analysis methodology to identify the key parameters that establish a quantitative relationship between the collected signals and cortisol concentration. Altogether, this work will contribute to the development of an innovative measurement device based on cutting-edge optical emission and detection technologies available at CEA Leti, combining precision, sensitivity, compactness, and thus compatibility with in situ use.
Development of 4D-STEM with variable tilts
The development of 4D-STEM (Scanning Transmission Electron Microscopy) has profoundly transformed transmission electron microscopy (TEM) by enabling the simultaneous recording of spatial (2D) and diffraction (2D) information at each probe position. These so-called “4D” datasets make it possible to extract a wide variety of virtual contrasts (bright-field imaging, annular dark-field imaging, ptychography, strain and orientation mapping) with nanometer-scale spatial resolution.
In this context, 4D-STEM with variable beam tilts (4D-STEMiv) is an emerging approach that involves sequentially acquiring electron diffraction patterns for different incident beam tilts. Conceptually similar to precession electron diffraction (PED), this method offers greater flexibility and opens new possibilities: improved signal-to-noise ratio, faster two-dimensional imaging at higher spatial resolution, access to three-dimensional information (orientation, strain, phase), and optimized coupling with spectroscopic analyses (EELS, EDX).
The development of 4D-STEMiv thus represents a major methodological challenge for the structural and chemical characterization of advanced materials, particularly in the fields of nanostructures, two-dimensional materials, and ferroelectric systems.
Infertility is a growing problem in all developed countries. The standard methods for the diagnostic of male infertility examine the concentration, motility and morphological anomalies of individual sperm cells. However, 40% of male infertility cases remain unexplained with the standard diagnostic tools.
In this thesis, we will explore the possibility to determine the male infertility causes from the detailed analysis of 3D trajectories and morphology of sperms swimming freely in the environment mimicking the conditions in the female reproductive tract. For this challenging task, we will develop a dedicated microscope based on holography for fast imaging and tracking of individual sperm cells. Along with classical numerical methods, we will use up-to date artificial intelligence algorithms for improving the imaging quality as well as for analysis of multi-dimensional data.
Throughout the project we will closely collaborate with medical research institute (CHU/IAB) specialized in Assisted Reproductive Technologies (ART). We will be examining real patient samples in order to develop a new tool for male infertility diagnosis.