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.
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.
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.
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.
Development and multiparametric monitoring of a microfluidic chip of the blood-brain barrier model
The blood-brain barrier (BBB) protects the brain by controlling exchanges between the blood and nervous tissue. However, current models struggle to accurately reproduce its complexity. This thesis aims at developing and evaluating a microfluidic chip of BBB model incorporating a real-time monitoring system that combines simultaneous optical and electrical measurements. The device will enable the study of permeability, transendothelial resistance and cellular response to various pharmacological or toxic stimuli. By combining microtechnologies, cell co-cultures and integrated sensors, this model of biological avatar will offer a more physiological and dynamic approach than conventional in vitro systems to improve understanding of the diffusion/permeation phenomena of therapeutic molecules. This project will contribute to the development of predictive tools for neuropharmacology, toxicology and research into neurodegenerative diseases.
Development and validation of surface haptics machine learning algorithms for touch and dexterity assessment in neurodevelopmental disorders
The aim of this PhD thesis is to develop new clinical assessment methods using surface haptics technologies, developed at CEA List, and machine learning algorithms for testing and monitoring tactile-motor integration. In particular, the thesis will investigate and validate the development of a multimodal analytics pipeline that converts surface haptics signals and dexterity exercises inputs (i.e. tactile stimulation events, finger kinematics, contact forces, and millisecond timing) into reliable, interpretable biomarkers of tactile perception and sensorimotor coupling, and then classify normative versus atypical integration patterns with clinical fidelity for assessment.
Expected results: a novel technology and models for the rapid and feasible measurement of tactile-motor deficits in clinical setting, with an initial validation in different neurodevelopmental disorders (i.e. first-episode psychosis, autism spectrum disorder, and dyspraxia). The methods developed and data collected will provide:
(1) an open, versioned feature library for tactile–motor assessment;
(2) classifiers with predefined operating points (sensitivity/specificity);
(3) and an on-device/edge-ready pipeline, i.e. able to run locally on a typical tablet hardware whilst meeting constraints on latency, computing, and data privacy. Success will be measured by reproducibility of features, clinically meaningful effect sizes, and interpretable decision logic that maps back to known neurophysiology rather than artefacts.
Magnetic Tunnel Junctions at Boundaries
Spin electronics, thanks to the additional degree of freedom provided by electron spin, enables the deployment of a rich physics of magnetism on a small scale, but also provides breakthrough technological solutions in the field of microelectronics (storage, memory, logic, etc.) as well as for magnetic field measurement.
In the field of life sciences and health, giant magnetoresistance (GMR) devices have demonstrated the possibility of measuring the very weak fields produced by excitable cells on a local scale (Caruso et al, Neuron 2017, Klein et al, Journal of Neurophysiology 2025).
Measuring the information contained in the magnetic component associated with neural currents (or magnetophysiology) can, in principle, provide a description of the dynamic, directional and differentiating neural landscape. It could pave the way for new types of implants, thanks to their immunity to gliosis and their longevity.
The current bottleneck is the very small amplitude of the signal produced (<1nT), which requires averaging the signal in order to detect it.
Tunnel magnetoresistances (TMR), in which a spin-polarised tunnel current is measured, offer sensitivity performance that is more than an order of magnitude higher than GMR. However, they currently have too high a level of low-frequency noise to be fully beneficial, particularly in the context of measuring biological signals.
The aim of this thesis is to push back the current limits of TMRs by reducing low-frequency noise, positioning them as break sensors for measuring very weak signals and exploiting their potential as amplifiers for small signals.
To achieve this objective, an initial approach based on exploring the materials composing the tunnel junction, in particular those of the so-called free magnetic layer, or on improving the crystallinity of the tunnel barrier, will be deployed. A second approach, consisting of studying the intrinsic properties of low-frequency noise, particularly in previously unexplored limits, at very low temperatures where intrinsic mechanisms are reached, will guide the most promising solutions.
Finally, the most advanced structures and approaches at the state of the art thus obtained will be integrated into devices that will provide the building blocks for going beyond the state of the art and offering new possibilities for spin electronics applications. These elements will also be integrated into systems for 2D (or even 3D) mapping of the activity of a global biological system (neural network) and for evaluating capabilities for clinical cases (such as epilepsy or motor rehabilitation).
It should be noted that these improved TMRs may have other applications in the fields of physical instrumentation, non-destructive testing, and magnetic imaging.
Optimization of gamma radiation detectors for medical imaging. Time-of-flight positron emission tomography
Introduction
Innovative functional imaging technologies are contributing to the CEA's ‘Medicine for the Future’ priority. Positron emission tomography (PET) is a nuclear medical imaging technique widely used in oncology and neurobiology. The decay of the radioactive tracer emits positrons, which annihilate into two photons of 511 keV. These photons are detected in coincidence and used to reconstruct the distribution of tracer activity in the patient's body.
We're proposing you to contribute to the development of an ambitious, patented technology: ClearMind. The first prototype is in our laboratories. This gamma photon detector uses a monolithic scintillating crystal of high density and atomic number, in which Cherenkov and scintillation photons are produced. These optical photons are converted into electrons by a photoelectric layer and multiplied in a MicroChannel plate. The induced electrical signals are amplified by gigahertz amplifiers and digitized by SAMPIC fast acquisition modules. The opposite side of the crystal will be fitted with a matrix of silicon photomultiplier (SiPM).
Today we have our first prototype, and we are preparing two more.
The proposed work
You will work in an advanced instrumentation laboratory in a particle physics environment .
The first step will be to optimize the "components" of ClearMind detectors, in order to achieve nominal performance. We'll be working on scintillating crystals, optical interfaces, photoelectric layers and associated fast photodetectors (MCP-PMT and SiPM), and readout electronics.
We will then characterize the performance of the prototype detectors on our measurement benches, which are under continuous development. The data acquired will be interpreted using in-house analysis software written in C++ and/or Python.
Finally, we will compare the physical behavior of our detectors to Monté-Carlo simulation software (Geant4/Gate).
A particular effort will be devoted to the development of ultra-fast scintillating crystals in the context of a European collaboration.
Supervision
The successful candidate will work under the joint supervision of Dominique Yvon and Viatcheslav Sharyy (DRF/IRFU & BIOMAPS). The CaLIPSO group at IRFU & BIOMAPS specializes in the development and characterization of innovative PET detectors, including detailed detector simulation. As part of the project, we are working closely with IJCLabs in Orsay, which is developing our readout and acquisition electronics, CEA/DM2S, which is working in particular on trusted AI algorithms, CPPM in Marseille, which is evaluating our detectors under PET imaging acquisition conditions, and UMR BIOMAPS (CEA/SHFJ), working on image calculation algorithms.
Requirements
Knowledge of the physics of particle-matter interaction, radioactivity and the principles of particle detectors is essential. A strong interest in instrumentation and laboratory work is recommended. Basic programming skills, e.g. C++, Gate/Geant4 physics simulation software, are important.
Skills acquired
Good knowledge of state-of-the-art particle detector and positron emission tomography technologies. Simulation principles and techniques for particle-matter interaction and detection systems. Analysis of complex data.
Contact
Dominique Yvon, dominique.yvon@cea.fr
Viatcheslav Sharyy, viatcheslav.sharyy@cea.fr