Development of the Compton-TDCR Method for Scintillator Metrology
The objectives of this PhD thesis lie upstream of the applied domain, specifically in the field of radionuclide metrology. The research aims to obtain essential information for a deeper understanding of scintillation mechanisms. This topic represents a new discipline within the national metrology laboratory, currently nonexistent in other laboratories, and focuses specifically on scintillator metrology. The work will be centered on instrumentation and data analysis, enabling a refined understanding of the underlying physical phenomena. The PhD will be co-supervised by Benoit Sabot (expert in radioactivity metrology) and Christophe Dujardin (expert in scintillation).
One of the primary experimental objectives of this PhD is the development and implementation of the new Compton-TDCR setup [7], designed for the absolute measurement of scintillation yield as a function of electron energy. This system will be designed using 3D printing technology and will integrate high-purity germanium (GeHP) detectors to enhance measurement precision. After characterizing these detectors in terms of energy resolution and efficiency, they will be integrated into the final experimental setup. The PhD candidate will be responsible for signal processing using a digital module generating List-Mode files. The data will then be analyzed using an existing Rust-based software with a Python interface, which is currently limited to four channels. Given that the new setup will incorporate up to three GeHP detectors in addition to three photomultiplier channels, the software must be adapted to ensure optimal processing of the acquired data. Following fine-tuning of the electronics and a series of experimental tests, the required software modifications will be implemented to enable full data exploitation from the platform.
Once this initial phase is completed and the platform is fully operational, the candidate will focus on investigating scintillation phenomena. The first studies will examine standard scintillating materials, such as organic (liquid or plastic) and inorganic scintillators. Subsequently, the research will extend to less explored materials, such as porous scintillators. This phase will involve close collaboration with the University of Lyon, particularly with the Institut Lumière Matière, where complementary measurements will be performed to refine the analysis of scintillation phenomena, complete the laboratory findings, and develop simulations that integrate various experimental approaches.
The ultimate goal of this setup is to establish a metrology methodology for scintillators, enabling access to the response curve of these materials as a function of the energy of electrons interacting within them, as well as their temporal properties. This work will pave the way for new ionizing radiation measurement techniques and will make a significant contribution to the scientific community in this field.
Towards eco innovative, sustainable and reliable piezoelectric technology
Are you looking for a Phd position at the intersection of eco-innovation and high-tech? This subject is for you!
You will participate in efforts aimed at reducing the environmental footprint of piezoelectric (PZE) technology applied to micro actuators and sensors, while maintaining optimal levels of electrical performance and reliability. Currently PZE technology primarily relies on PZT material (Pb(Zr,Ti)O3) which contains lead, as well as electrodes made from materials such as Pt, Ru, and Au, along with doping elements like La, Mn and Nb to enhance piezoelectric properties and electrical performance. These materials not only come with a significant ecological cost but are also facing proven or imminent shortages. In the context of the necessary frugality associated with the energy transition, this PhD position aims to explore more environmentally friendly and sustainable microsystem technologies. The research will create a comparative analysis assessing the ecological footprint, electromechanical performance, and reliability of existing technologies (with lead) versus those under development (lead free). To achieve these objectives, you will employ Life Cycle Analyses (LCA), electromechanical measurements, and reliability tests (accelerated aging tests).
This interdisciplinary research will encompass fields such as eco design materials science, and microelectronic manufacturing processes You will benefit from the support of laboratories specializing in microsystems manufacturing and integration processes, as well as electrical characterization and reliability Collaboration with the “eco innovation” unit at CEA Leti will also enhance the resources available for this project.
Micro-needles functionalized with aptamers for the optical detection of cortisol
Compact, wearable medical devices, by offering autonomous and continuous monitoring of biomarkers, open the way to precise monitoring of pathologies outside of care centers and to a personalized therapeutic approach. The thesis project aims to develop wearable sensors based on microneedles (MNs) made of biomaterials for the minimally invasive detection of cortisol in the interstitial fluid (ISF) of the skin. Cortisol is one of the important biomarkers of physical and psychological stress, and is linked to the development of chronic diseases. ISF, a very rich source of biomarkers, offers an alternative to blood as a minimally invasive biofluid for cortisol quantification, and can be continuously analyzed by microneedle devices. Thus, swelling microneedles made of crosslinked biopolymer hydrogel have been developed at CEA-Leti over the last three years for ISF collection and analysis. The objective of the project will be to functionalize the hydrogel with a cortisol-sensitive aptamer molecular beacon, whose fluorescence will be activated in the specific presence of this metabolite, drawing on the expertise of the DPM NOVA team. Wearable optical sensors based on cortisol-sensitive MN patches will be designed, exploring two configurations: MN patches entirely made of hydrogel, and hybrid MN patches comprising an optical waveguide biopolymer and a cortisol-sensitive hydrogel coating. Different needle/waveguide shapes will be explored to optimize the fluorescence detection performance of the biosensors. The ability of the devices to puncture a skin model, sample artificial ISF, and detect the target will also be evaluated. The study will include biocompatibility tests, as well as a comparison with current methods for measuring serum cortisol by immunoassay.
Out-of-Distribution Detection with Vision Foundation Models and Post-hoc Methods
The thesis focuses on improving the reliability of deep learning models, particularly in detecting out-of-distribution (OoD) samples, which are data points that differ from the training data and can lead to incorrect predictions. This is especially important in critical fields like healthcare and autonomous vehicles, where errors can have serious consequences. The research leverages vision foundation models (VFMs) like CLIP and DINO, which have revolutionized computer vision by enabling learning from limited data. The proposed work aims to develop methods that maintain the robustness of these models during fine-tuning, ensuring they can still effectively detect OoD samples. Additionally, the thesis will explore solutions for handling changing data distributions over time, a common challenge in real-world applications. The expected results include new techniques for OoD detection and adaptive methods for dynamic environments, ultimately enhancing the safety and reliability of AI systems in practical scenarios.
Diamond Beam Monitor for FLASH Therapy
Optimizing tumor dose delivery requires advanced treatment techniques. One promising approach focuses on refining beam delivery through ultra-high dose rate irradiation (UHDR), with temporal optimization being a key strategy. Recent studies highlight the effectiveness of FLASH irradiation using electrons, demonstrating similar tumor inhibition capabilities as gamma rays but with reduced damage to healthy tissue. To fully harness this potential, we are exploring innovative beams, such as high energy electron beams, which offer instantaneous dose rates and per-pulse doses many times higher than those produced by conventional radiation sources. However, accurately monitoring and measuring these beams remains a significant challenge, primarily due to the high dose rate.
The Sensors and Instrumentation Laboratory (CEA-List) will collaborate with the Institut Curie as part of the FRATHEA project. We propose the development of a novel diamond-based monitor, connected to associated electronics, to achieve precise measurements of dose and beam shape for high-rate electron and proton beams. Interdisciplinary experimental techniques, including diamond growth, device microfabrication, device characterization under radioactive sources, and final evaluation with electron beam, will be used for prototyping and testing the diamond beam monitor.
As part of the FRATHEA project, the PhD student will work on the following tasks:
• Growth of optimized single-crystal chemical vapor-deposited (scCVD) diamond structures
• Characterization of the electronic properties of the synthesized diamond materials
• Estimation of the dose response characteristics of a simplified prototypes
• Fabrication of a pixelated beam monitor
• Participation in beam times at the Institut Curie (an other institutes) for devices testing in clinical beams
Required Skills:
• Strong background in semiconductor physics and instrumentation
• Knowledge of radiation detectors and radiation-matter interactions
• Ability to work effectively in a team and demonstrate technical rigor in measurements
Additional Skills:
• Knowledge of electronics, including signal processing, amplifiers, oscilloscopes, etc.
• Familiarity with device fabrication and microelectronics
• Previous experience working with diamond materials
Profile:
• Master's level (M2) or engineering school, with a specialization in physical measurements
• Adherence to radiation protection regulations (category B classification required)
PhD Duration: 3 years
Start Date: Last semester of 2025
Contact:
Michal Pomorski : michal.pomorski@cea.fr
Guillaume Boissonnat: guillaume.boissonnat@cea.fr
Design, fabrication, and characterization of GeSn alloy-based laser sources for mid-infrared silicon photonics
You will design and fabricate laser and LED sources based on GeSn alloy in a cleanroom environment. These novel group-IV direct-bandgap materials, epitaxially grown on 200 mm Si wafers, are considered CMOS-compatible and hold great promise for the development of low-cost mid-infrared light sources. You will characterize these light sources using a mid-infrared optical test bench, with the goal of their future integration into a Germanium/Silicon photonic platform. Additionally, you will assess the feasibility of gas detection within a concentration range from a few dozen to several thousand ppm.
The objectives of the PhD are to:
• Design efficient GeSn (Si) stack structures that confine both electrons and holes while providing strong optical gain.
• Evaluate the optical gain under optical pumping and electrical injection at different strain levels and doping concentrations.
• Design and fabricate laser cavities with strong optical confinement.
• Characterize the fabricated devices under optical and electrical injection as a function of their strain state at both room and low temperatures.
• Achieve electrically pumped continuous-wave group-IV lasers.
• Understand the physical phenomena that may impact the material and device performance for light emission.
• Characterize the best-fabricated devices for low-cost environmental gas detection applications.
This work will involve collaborations with international laboratories working on the same dynamic research topic.
Attacker model validation for laser-based attacks on integrated circuits
The security of embedded systems is nowadays a fundamental issue in many domains: IoT, Automotive, Aeronautics, among others. The physical attacks are a specific threat assuming a physical access to the target. In particular, fault injection attacks on the integrated circuits (IC) allows to disturb the system in order to retrieve secret material or to achieve a special goal such as by passing secure boot to execute malicious code. Due to their powerful capacities to defeat system security, developers must protect their system against such attack to be compliant with security standards such as Common Criteria and FIPS.
Within the context of continuous downscaling of silicon technologies, and with the transition to FD-SOI technologies, the vulnerability model of an IC must be drastically revised, from the transistor level up to the complex digital circuits one. In this PhD we propose to study the attacker model validation in the at the latter level. The objective is to contribute to the definition of a model of vulnerability after synthesis-of a RTL description of a circuit (for example a core processor) in a 22 nm FD-SOI technology. These models will contribute to define the attacker model injected as input in formal-based verification tools. The candidate will have to define a methodology to characterize with laser experiments the multilayer and heterogenous models in order to provide a quantitative analysis of their limit of validity. The methodology will be tested on ASIC realized by CEA for R&D projects allowing having a full control and knowledge of the architecture, of the design and synthesis parameters and the executed codes.
High mobility mobile manipulator control in a dynamic context
The development of mobile manipulators capable of adapting to new conditions is a major step forward in the development of new means of production, whether for industrial or agricultural applications. Such technologies enable repetitive tasks to be carried out with precision and without the constraints of limited workspace. Nevertheless, the efficiency of such robots depends on their adaptation to the variability of the evolutionary context and the task to be performed. This thesis therefore proposes to design mechanisms for adapting the sensory-motor behaviors of this type of robot, in order to ensure that their actions are appropriate to the situation. It envisages extending the reconfiguration capabilities of perception and control approaches through the contribution of Artificial Intelligence, here understood in the sense of deep learning. The aim is to develop new decision-making architectures capable of optimizing robotic behaviors for mobile handling in changing contexts (notably indoor-outdoor), and for carrying out a range of precision tasks.
Integrated System for Adaptive Antenna Tuning and Synthesized Impedance in the Sub-6 GHz Band for Next-Generation RF Systems.
The growing adoption of sub-6 GHz RF systems for 5G, IoT, and wearable technologies has created a critical demand for compact, efficient, and adaptive solutions to enhance energy transfer, mitigate environmental detuning effects, and enable advanced sensing capabilities. This thesis proposes an innovative system-on-chip (SoC) that integrates an Antenna Tuning Unit (ATU) and a Synthesized Impedance Module (SIM) to address these challenges. By combining in-situ impedance measurement and dynamic re-adaptation, the system resolves a key limitation of miniature antennas—their extreme sensitivity to environmental perturbations, such as proximity to the human body or metal surfaces. Moreover, the integration of a Synthesized Impedance Module brings additional versatility by enabling the emulation of complex loads. This capability not only optimizes energy transfer but also allows for advanced functionality, such as material characterization and environmental sensing around the antenna.
A central focus of this research is the co-integration of a Vector Network Analyzer (VNA) with a broadband post-matching network (PMN) and a Synthesized Impedance Module. This combined architecture provides real-time impedance monitoring, dynamic tuning, and the generation of specific impedance profiles critical for characterizing the antenna's response under various scenarios. Guaranteed operation in the 100 MHz–6 GHz band is achieved while maintaining low power consumption through efficient duty cycling.
. Profile Sought : are you passionate about electronics and microelectronics and eager to contribute to a major technological breakthrough? We are looking for a motivated and curious candidate with the following qualifications:
. Education
Graduate of an engineering school or holder of a master’s degree in electronics or microelectronics.
Technical Skills
Strong knowledge of transistor technologies (CMOS, Bipolar, GaN…).
Expertise in analog/RF design.
Experience with design tools such as ADS and/or Cadence.
Programming
Basic skills in Python, MATLAB, or similar programming languages.
Additional Experience
Prior experience in integrated circuit design would be a valuable asset.
. Why Apply: you will have the opportunity to work on cutting-edge technologies in an innovative and collaborative research environment. You will be guided by renowned experts in the field to tackle exciting scientific and technical challenges.
Contacts: PhD. Ghita Yaakoubi Khbiza: ghita.yaakoubikhbiza@cea.fr, HDR. Serge Bories: serge.bories@cea.fr
AI-assisted generation of Instruction Set Simulators
The simulation tools for digital architectures rely on various types of models with different levels of abstraction to meet the requirements of hardware/software co-design and co-validation. Among these models, higher-level ones enable rapid functional validation of software on target architectures.
Developing these functional models often involves a manual process, which is both tedious and error-prone. When low-level RTL (Register Transfer Level) descriptions are available, they serve as a foundation for deriving higher-level models, such as functional ones. Preliminary work at CEA has resulted in an initial prototype based on MLIR (Multi-Level Intermediate Representation), demonstrating promising results in generating instruction execution functions from RTL descriptions.
The goal of this thesis is to further explore these initial efforts and subsequently automate the extraction of architectural states, leveraging the latest advancements in machine learning for EDA. The expected result is a comprehensive workflow for the automatic generation of functional simulators (a.k.a Instruction Set Simulators) from RTL, ensuring by construction the semantic consistency between the two abstraction levels.