Development of the Fast Gamma Radiation Detectors for the Time-of-Flight Positron Emission Tomography

Innovative functional imaging technologies are a key component of the "Medicine for the Future" program, one of the main strategic priorities of the CEA. We are seeking a highly motivated PhD student to join our team and contribute to the next generation of Positron Emission Tomography (PET) systems. PET is a powerful nuclear medical imaging technique widely used in oncology and neurobiology. It involves the decay of a radioactive tracer, which emits positrons that annihilate with electrons, producing two 511 keV photons. These photons are detected in coincidence and used to reconstruct the spatial distribution of the tracer within the patient's body. Accurate timing of these coincidence signals, known as Time-of-Flight (TOF) information, is crucial for enhancing image quality by localizing the annihilation point along the line of response. The development of ultra-fast detectors is a central challenge in modern PET technology.
This PhD thesis offers an exciting opportunity to contribute to the development of cutting-edge technologies for TOF-PET within the ClearMind project. In this innovative detector concept, Cherenkov and scintillation photons are produced in a monolithic lead tungstate (PbWO4) crystal. These photons are converted into electrons by a photoelectric layer and multiplied in a microchannel plate. The opposite surface of the crystal is instrumented with a matrix of silicon photomultipliers. The resulting electrical signals are amplified by gigahertz bandwidth amplifiers and digitized by fast SAMPIC acquisition modules. Advanced machine-learning techniques are applied to the complex acquired signals to reconstruct the time and coordinates of the gamma interaction in the crystal. In addition, the thesis will investigate the potential of alternative fast scintillating crystals and study their timing, light yield, and compatibility with the photodetectors, to push the performance of future TOF-PET systems even further.

3D Bio-Based mRNA-Enriched Materials for Bone Regeneration: Optimizing Osteogenic Differentiation Using AI-Based Methods

This PhD project aims to develop bioresorbable scaffolds functionalized with mRNA using a polycation, poly(L-lysine), and hyaluronic acid, arranged in different structural architectures.
The originality of this approach lies in two key aspects. First, these biomaterials will incorporate mRNA sequences encapsulated within nanoparticles, encoding proteins that play a crucial role in bone regeneration, such as VEGF (Vascular Endothelial Growth Factor) and BMP (Bone Morphogenetic Proteins). Second, the project will investigate how the three-dimensional organization of the polymer network influences the biological performance of the scaffold.
The overall objective is to transform cells located at the injury site into localized therapeutic protein factories through in situ transfection, enabling sustained, localized production of the therapeutic proteins at physiological concentrations.
Artificial intelligence (AI) approaches will be used as computer-aided design tools to analyze and exploit the experimental data. These methods will guide the optimization of scaffold design—including composition, architecture, and mRNA loading—to maximize osteogenic differentiation while reducing the number of experimental iterations required.
This work is part of an interdisciplinary and collaborative research project. The PhD candidate will also contribute to data analysis, the writing of scientific publications, and the dissemination of research findings at national and international conferences.