Advanced reconstruction methods for cryo-electron tomography of biological samples
Cryo-electron tomography (CET) is a powerful technique for the 3D structural analysis of biological samples in their near-native state. CET has seen remarkable advances in instrumentation in the last decade but the classical weighted back-projection (WBP) remains by far the standard CET reconstruction method. Due to radiation damage and the limited tilt range within the microscope, WBP reconstructions suffer from low contrast and elongation artifacts, known as ‘missing wedge’ (MW) artifacts. Recently, there has been a revival of interest in iterative approaches to improve the quality and hence the interpretability of the CET data.
In this project, we propose to go beyond the state-of-the-art in CET by (1) applying curvelet- and shearlet-based compressed sensing (CS) algorithms, and (2) exploring deep learning (DL) strategies with the aim to denoise et correct for the MW artifacts. These approaches have the potential to improve the resolution of the CET reconstructions and facilitate the segmentation and sub-tomogram averaging tasks.
The candidate will conduct a comparative study of iterative algorithms used in life science, and CS and DL approaches optimized in this project for thin curved structures.
Quantum dot auto-tuning assisted by physics-informed neural networks
Quantum computers hold great promise for advancing science, technology, and society by solving problems beyond classical computers' capabilities. One of the most promising quantum bit (qubit) technologies are spin qubits, based on quantum dots (QDs) that leverage the great maturity and scalability of semiconductor technologies. However, scaling up the number of spin qubits requires overcoming significant engineering challenges, such as the charge tuning of a very large number of QDs. The QD tuning process implies multiple complex steps that are currently performed manually by experimentalists, which is cumbersome and time consuming. It is now crucial to address this problem in order to both accelerate R&D and enable truly scalable quantum computers.
The main goal of the postdoctoral project is to develop a QD automatic tuning software combining Bayesian neural networks and a QD physical model fitted on CEA-Leti’s device behavior. This innovative approach leveraging the BayNN uncertainty estimations and the predictive aspect of QD models will enable to achieve fast and non-ideality-resilient automatic QD tuning solutions.
Desigon of fully "on the fly" reconfigurable sensor interfaces based on network of oscillators
The availability of low-cost sensors and the development of 5G and 6G radios are responsible of a tremendous increase of the number of wireless sensor networks. It is highly necessary to design ultra-low power sensor interface ICs to limit the energy impact of such applications. One solution consists in placing Artificial Intelligence near the sensor (Edge-AI) to limit the transmission of useless information. In addition, the development of reconfigurable sensor interfaces ICs could help in decreasing the development cost of these applications. On top of that, getting solution of on-the-fly reconfigurable sensor interfaces could have a crucial impact on Edge-IA accuracy as it could bring the possibility to adapt the analog signal conditioning depending on the application context.
The postdoc candidate will contribute to the design of a CMOS integrated circuit able to condition a large number of sensors and which will be fully reconfigurable in gain, bandwidth, and that will implement reconfigurable ADCs and or reconfigurable neural network. For that purpose, the postdoc candidate will work on time-domain sensor interfaces based on Injection Locked Oscillators (ILOs) that have been already demonstrated at CEA Leti. The post doc candidate will have to design and test an oscillator network and will have to demonstrate its low-power behaviour as well as its on-the-fly configurability by maping an ultra-low power key word spotting application. The postdoc will take place in laboratory LGECA of CEA which is dedicated to analog mixed design for sensors applications.
Digital correction of the health status of an electrical network
Cable faults are generally detected when communication is interrupted, resulting in significant repair costs and downtime. Additionally, data integrity becomes a major concern due to the increased threats of attacks and intrusions on electrical networks, which can disrupt communication. Being able to distinguish between disruptions caused by the degradation of the physical layer of an electrical network and an ongoing attack on the energy network will help guide decision-making regarding corrective operations, particularly network reconfiguration and predictive maintenance, to ensure network resilience. This study proposes to investigate the relationship between incipient faults in cables and their impact on data integrity in the context of Power Line Communication (PLC). The work will be based on deploying instrumentation using electrical reflectometry, combining distributed sensors and AI algorithms for online diagnosis of incipient faults in electrical networks. In the presence of certain faults, advanced AI methods will be applied to correct the state of the health of the electrical network's physical layer, thereby ensuring its reliability.
Attack detection in the electrical grid distributed control
To enable the emergence of flexible and resilient energy networks, we need to find solutions to the challenges facing these networks, in particular digitization and the protection of data flows that this will entail, and cybersecurity issues.
In the Tasting project, and in collaboration with RTE, the French electricity transmission network operator, your role will be to analyze data protection for all parties involved. The aim is to verify security properties on data in distributed systems, taking into account that those induce a number of uncertainties.
To this end, you will develop a tool-based methodology for protecting the data of power grid stakeholders. The approach will be based on formal methods, in particular runtime verification, applied to a distributed control system.
This postdoc position is part of the TASTING project, which aims to meet the key challenges of modernizing and securing power systems. This 4-year project, which started in 2023, addresses axis 3 of the PEPR TASE call “Technological solutions for the digitization of intelligent energy systems”, co-piloted by CEA and CNRS, which aims to generate innovations in the fields of solar energy, photovoltaics, floating wind power and for the emergence of flexible and resilient energy networks. The targeted scientific challenges concern the ICT infrastructure, considered as a key element and solution provider for the profound transformations that our energy infrastructures will undergo in the decades to come.
The project involves two national research organizations, INRIA and CEA through its technological research institute CEA-List. Also involved are 7 academic laboratories: G2Elab, GeePs, IRIT, L2EP, L2S and SATIE, as well as an industrial partner, RTE, which is supplying various use cases.
Design and test of a system for neuromodulation based on focused ultrasounds
We recently focused our interest on Brain Computer interfaces to cope with motor handicap and propose to patient with spinal cord injury devices for motor substitution , , .
In parallel, several groups at CEA investigated the advantages of ultrasound for medical imagery , diagnosis or developed miniaturized ultrasound sources (CMUT/PMUT).
The post-doctoral position is funded in the framework of a LETI-Carnot project on this innovative thematic. The project aims to (1) build a test bench to validate the compatibility of ultrasound neuromodulation with neural recording devices (2) design optimization for fine resolution and low power (3) specify a system combining neuromodulation by focused ultrasound and electrophysiological recording.
The post-doctoral student, with the help of a team of experts in the fields of ultrasounds and biomedical system, will be in charge of the modelling of acoustic waves propagation, compatibility tests with recording system, system design and experimental validation of resolution or efficiency. The concepts underlying the project could in the future be applicable to new implantable or wearable devices for a combination of focused ultrasound for neuromodulation and neural recording.
Development of an innovative method for ultrasound imaging of velocity fields in flows behind opaque walls
Today, the only solutions on the market for measuring 2D velocity fields are laser-based optical methods (such as particle imaging velocimetry: PIV).
These are limited by the need for optical access to the flow and are therefore inapplicable on opaque fluids (such as liquid metals) or through opaque pipes (such as metal pipes, the majority in industry).
To overcome this limitation and meet new challenges (in research and industry) it is possible to rely on acoustic imaging methods.
The LISM (CEA Cadarache Instrumentation Laboratory) has been working for several years on the development of an industrial acoustic PIV (or echo-PIV) method.
An initial thesis has led to significant progress, and the CEA is now planning to market echo-PIV scanners through a start-up project.
However, there are still a number of hurdles to overcome, in particular that of imaging through walls with high acoustic impedance differences.
Your main objective will be to remove these obstacles. This mission will be structured as follows:
- Bibliographical study and familiarisation with the echo-PIV method
- Numerical study and development of a solution to resolve the problems of energy transmission through the metal wall
- Experimental validation of the detection of microscopic reflectors through a metal wall
- Numerical study and development of a solution to the problem of multiple reflection within the metal wall, leading to poor reconstruction of the final image
- Experimental validation of the solution to the reflection problem
- Adaptation of the acoustic imaging method to simultaneously resolve the transmission and reflection problems
- Publication in scientific journals (and/or patents)
Characterisation of fast transient phenomena using X-ray phase contrast imaging
The aim of this post-doctorate is to develop a measurement chain dedicated to the observation and characterisation of Rapid Transient Phenomena (RTP) using X-ray phase contrast imaging (XPCI). The challenge is to provide a measurement system that can be deployed in the laboratory on a wide range of experiments that cannot be moved to the synchrotron. The performance targets are justified by the problems associated with additive manufacturing, the propagation of shock waves in low-density polymers, and the diagnosis of carbon composite materials impacted by an electric arc.
For objects with low absorption, such as low-density polymers, liquids or plasmas, conventional X-ray imaging, which provides contrast due to the absorption cross-sections variations, is insufficient. To complete absorption, it is possible to exploit the phase of X-rays, which provides better detection of inhomogeneities and interfaces. The method used here to measure the phase is the multilateral shearing interferometry (IDML). It uses a single two-dimensional checkerboard phase grating that generates a reference interference pattern on the detector. The introduction ofan object between the grating and the detector modifies the reference interference pattern, which is then analysed by Fourier transform to reconstruct the phase image. By requiring only a single phase grid and exhibiting minimal X-ray flux loss, this method has favourable intrinsic characteristics in terms of sensitivity, robustness, ease of alignment and versatility, for application to dynamic imaging.
Optomechanical resonators in chaotic regime for cryptography in optical datacoms
The aim of the post doc is to explore the use of optomechanical resonators placed in a chaotic regime to secure optical communications. It is part of a project from the CEA's research-at-risk program, selected in July 2024. A key point is to obtain a highly non-linear regime, favored by specific geometries, necessary for the richness of chaos. Exploiting the unique properties of chaos for secure data transfer will be explored by the postdoc as part of a working group.
With the advent of the quantum computer, current techniques for securing information exchange become largely compromised, necessitating the development of post-quantum cryptography techniques. Beyond software approaches, new hardware concepts have emerged, such as chaotic cryptography. In this context, it is becoming essential to develop chaos sources that are high-quality (richness of parameter space), compatible with existing communication systems and compact. While lasers are a well-known source of chaos, optomechanical systems seem particularly well suited to this application, as the mechanical domain provides an enriched parameter space, while retaining high data throughput and a direct connection with optical communications systems. The postdoc will explore the suitability of chaotic optomechanical devices for implementing hardware cryptography.
Study of the specific features of highly distributed architectures for decision and control requirements
Our electricity infrastructure has undergone and will continue to undergo profound changes in the coming decades. The rapid growth in the share of renewables in electricity generation requires solutions to secure energy systems, especially with regard to the variability, stability and balancing aspects of the electricity system and the protection of the grid infrastructure itself. The purpose of this study is to help design new decision-making methods, specially adapted to highly distributed control architectures for energy networks. These new methods will have to be evaluated in terms of performance, resilience, robustness and tested in the presence of various hazards and even byzantines.