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.
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.
Modelling/Simulation of the synthesis of anti-corrosion coatings using the MOCVD process for low-carbon energy production
The durability of materials used in many areas of energy production is limited by their degradation in the operating environment, which is often oxidising and at high temperature. This is particularly true of High Temperature Electrolysers (HTE) for the production of ‘green’ hydrogen, or the fuel cladding used in nuclear reactors to produce electricity. Anti-corrosion coatings can/should be applied to improve the lifespan of these installations, thereby conserving resources. A process for synthesising coatings using a reactive vapour route with liquid organometallic precursors (DLI - MOCVD) appears to be a very promising process.
The aim of this thesis is to model and simulate the DLI-MOCVD coating synthesis process for the two applications proposed above. Simulation results (deposition rate, deposit composition, spatial homogeneity) will be compared with experimental results from large-scale ‘pilot’ reactors at the CEA in order to optimise the model's input parameters. On the basis of this CFD simulation/experiments dialogue, the optimum conditions for deposition on a scale 1 component will be proposed. A coupling between CFD simulations and Machine Learning will be developed to accelerate the change of scale and the optimisation of scale 1 deposits.
Radiological large-scale accident dosimetry: use of EPR spectroscopy for population triage by measurements of smartphone screens
In the event of a large-scale radiological emergency involving sources of external irradiation, methods are needed to identify which members of the population have been exposed and require priority care. To date, there are no operational methods for such sorting. Smartphone touch screen lenses retain traces of ionizing radiation through the formation of so-called “radiation-induced” defects.Measuring and quantifying these punctual defects, in particular by electron paramagnetic resonance (EPR) spectroscopy, makes itpossible to estimate the dose deposited in the glass, and thus the exposure associated with irradiation. The thesis work proposed herefocuses in particular on the alkali-aluminosilicate glasses used in cell phone touch screens, which are currently the best candidates fordeveloping new measurement capabilities in the context of accidents involving large numbers of victims.
We will focus in particular on identifying point defects as a function of the glass model used in smartphones by simulating EPR spectra in order to optimize the proposed dosimetry method.
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
Point Spread Function Modelling for Space Telescopes with a Differentiable Optical Model
Context
Weak gravitational lensing [1] is a powerful probe of the Large Scale Structure of our Universe. Cosmologists use weak lensing to study the nature of dark matter and its spatial distribution. Weak lensing missions require highly accurate shape measurements of galaxy images. The instrumental response of the telescope, called the point spread function (PSF), produces a deformation of the observed images. This deformation can be mistaken for the effects of weak lensing in the galaxy images, thus being one of the primary sources of systematic error when doing weak lensing science. Therefore, estimating a reliable and accurate PSF model is crucial for the success of any weak lensing mission [2]. The PSF field can be interpreted as a convolutional kernel that affects each of our observations of interest, which varies spatially, spectrally, and temporally. The PSF model needs to be able to cope with each of these variations. We use specific stars considered point sources in the field of view to constrain our PSF model. These stars, which are unresolved objects, provide us with degraded samples of the PSF field. The observations go through different degradations depending on the properties of the telescope. These degradations include undersampling, integration over the instrument passband, and additive noise. We finally build the PSF model using these degraded observations and then use the model to infer the PSF at the position of galaxies. This procedure constitutes the ill-posed inverse problem of PSF modelling. See [3] for a recent review on PSF modelling.
The recently launched Euclid survey represents one of the most complex challenges for PSF modelling. Because of the very broad passband of Euclid’s visible imager (VIS) ranging from 550nm to 900nm, PSF models need to capture not only the PSF field spatial variations but also its chromatic variations. Each star observation is integrated with the object’s spectral energy distribution (SED) over the whole VIS passband. As the observations are undersampled, a super-resolution step is also required. A recent model coined WaveDiff [4] was proposed to tackle the PSF modelling problem for Euclid and is based on a differentiable optical model. WaveDiff achieved state-of-the-art performance and is currently being tested with recent observations from the Euclid survey.
The James Webb Space Telescope (JWST) was recently launched and is producing outstanding observations. The COSMOS-Web collaboration [5] is a wide-field JWST treasury program that maps a contiguous 0.6 deg2 field. The COSMOS-Web observations are available and provide a unique opportunity to test and develop a precise PSF model for JWST. In this context, several science cases, on top of weak gravitational lensing studies, can vastly profit from a precise PSF model. For example, strong gravitational lensing [6], where the PSF plays a crucial role in reconstruction, and exoplanet imaging [7], where the PSF speckles can mimic the appearance of exoplanets, therefore subtracting an accurate and precise PSF model is essential to improve the imaging and detection of exoplanets.
PhD project
The candidate will aim to develop more accurate and performant PSF models for space-based telescopes exploiting a differentiable optical framework and focus the effort on Euclid and JWST.
The WaveDiff model is based on the wavefront space and does not consider pixel-based or detector-level effects. These pixel errors cannot be modelled accurately in the wavefront as they naturally arise directly on the detectors and are unrelated to the telescope’s optic aberrations. Therefore, as a first direction, we will extend the PSF modelling approach, considering the detector-level effect by combining a parametric and data-driven (learned) approach. We will exploit the automatic differentiation capabilities of machine learning frameworks (e.g. TensorFlow, Pytorch, JAX) of the WaveDiff PSF model to accomplish the objective.
As a second direction, we will consider the joint estimation of the PSF field and the stellar Spectral Energy Densities (SEDs) by exploiting repeated exposures or dithers. The goal is to improve and calibrate the original SED estimation by exploiting the PSF modelling information. We will rely on our PSF model, and repeated observations of the same object will change the star image (as it is imaged on different focal plane positions) but will share the same SEDs.
Another direction will be to extend WaveDiff for more general astronomical observatories like JWST with smaller fields of view. We will need to constrain the PSF model with observations from several bands to build a unique PSF model constrained by more information. The objective is to develop the next PSF model for JWST that is available for widespread use, which we will validate with the available real data from the COSMOS-Web JWST program.
The following direction will be to extend the performance of WaveDiff by including a continuous field in the form of an implicit neural representations [8], or neural fields (NeRF) [9], to address the spatial variations of the PSF in the wavefront space with a more powerful and flexible model.
Finally, throughout the PhD, the candidate will collaborate on Euclid’s data-driven PSF modelling effort, which consists of applying WaveDiff to real Euclid data, and the COSMOS-Web collaboration to exploit JWST observations.
References
[1] R. Mandelbaum. “Weak Lensing for Precision Cosmology”. In: Annual Review of Astronomy and Astro- physics 56 (2018), pp. 393–433. doi: 10.1146/annurev-astro-081817-051928. arXiv: 1710.03235.
[2] T. I. Liaudat et al. “Multi-CCD modelling of the point spread function”. In: A&A 646 (2021), A27. doi:10.1051/0004-6361/202039584.
[3] T. I. Liaudat, J.-L. Starck, and M. Kilbinger. “Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies”. In: Frontiers in Astronomy and Space Sciences 10 (2023). doi: 10.3389/fspas.2023.1158213.
[4] T. I. Liaudat, J.-L. Starck, M. Kilbinger, and P.-A. Frugier. “Rethinking data-driven point spread function modeling with a differentiable optical model”. In: Inverse Problems 39.3 (Feb. 2023), p. 035008. doi:10.1088/1361-6420/acb664.
[5] C. M. Casey et al. “COSMOS-Web: An Overview of the JWST Cosmic Origins Survey”. In: The Astrophysical Journal 954.1 (Aug. 2023), p. 31. doi: 10.3847/1538-4357/acc2bc.
[6] A. Acebron et al. “The Next Step in Galaxy Cluster Strong Lensing: Modeling the Surface Brightness of Multiply Imaged Sources”. In: ApJ 976.1, 110 (Nov. 2024), p. 110. doi: 10.3847/1538-4357/ad8343. arXiv: 2410.01883 [astro-ph.GA].
[7] B. Y. Feng et al. “Exoplanet Imaging via Differentiable Rendering”. In: IEEE Transactions on Computational Imaging 11 (2025), pp. 36–51. doi: 10.1109/TCI.2025.3525971.
[8] Y. Xie et al. “Neural Fields in Visual Computing and Beyond”. In: arXiv e-prints, arXiv:2111.11426 (Nov.2021), arXiv:2111.11426. doi: 10.48550/arXiv.2111.11426. arXiv: 2111.11426 [cs.CV].
[9] B. Mildenhall et al. “NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis”. In: arXiv e-prints, arXiv:2003.08934 (Mar. 2020), arXiv:2003.08934. doi: 10.48550/arXiv.2003.08934. arXiv:2003.08934 [cs.CV].