Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PostDocs : selection by topics

Compressed Sensing for ultrasonic imaging: disruptive method development and prototyping

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Méthodes CND



In non-destructive ultrasonic testing, multi-element sensors are used for the inspection of structures to ensure the safety of people and infrastructures. Currently, one of the driving factor of an ultrasonic method is the number of elements of the sensor, influencing the speed and efficiency of the inspection but also the cost and the volume of the equipment. This project aims at developing a prototype of a multi-element sensor with a limited number of elements compared to current state of the art equipment, without losing imaging resolution. To achieve this goal, Compressed Sensing (CS), a recent technique of signal processing allowing to go beyond the traditional sampling theorems and to reconstruct data from severely undersampled measurements, will be used. The ultrasonic inspection procedure will need to be entirely rethought to meet the CS requirements, specifically the sparsity of the measured data and the incoherence of the measurement process. The expected results is a significant reduction (of the order of 5) of the number of elements to conduct imaging, which would be a true revolution in NDT with direct applications in various industrials sectors. The following laboratories, all located in Saclay (France) of the CEA (the French atomic commission), will participate to the project: the NDT department for its expertise in multi-element ultrasonic testing and Neurospin and Cosmostat for their expertises in the field of CS, mainly applied to medical RMI imaging and astrophysics, respectively. The collaboration between these three labs, each among the worldwide leading institutes in their respective fields, will ensure the creation of a new and disruptive family of sensors.

PET/MRI nonparametric bayesian bootstrap reconstruction


Laboratoire Modélisation et Simulation des Systèmes



Very recently, Bayesian statistical researchers have suggested an alternative way to learn from large datasets. First, they move away from the conventional framework of Bayesian inference by replacing the generative model with a nonparametric prior directly related to the data distribution. The parameters are then inferred by minimizing a bunch of randomized costs functions. As part of this 18-month post-doctoral fellowship (Physicancer project, led by IGR), we propose to assess the problem of PET/MRI reconstruction in this Bayesian nonparametric learning framework. The study covers the performance assessment of the approach on realistic synthetic dataset by comparison to conventional techniques. Transfer to the Castor environment will be considered in collaboration with researchers of SHFJ. In addition, an analysis of the asymptotic properties of this estimator (concentration of the posterior distribution) will be carried in collaboration with the Laboratoire de Mathématiques d'Orsay (LMO).

Nanophotonics applied to ultrasensitive biomolecular detection

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Chimie des Matériaux et des Interfaces



This project proposes to develop an array of highly sensitive and specific detectors, based on nanowire photodetectors to target single molecule detection (SMD) and biological analysis applications involving a protocol without amplification. Nanowire arrays have the potential to improve the detection limit of DNA strands functionalized with quantum dots markers, without the need for amplification. They are CMOS compatible and will allow ultra-compact integration. Thanks to their fast response and the ability to create dense arrays over large areas, nanowire photodetector are therefore an interesting approach to detecting rare events (SMD). Nanowire geometry is an interesting approach to optimize the speed-response trade-off. The first objective of this project will be to explore the physical mechanisms that determine the performance of semiconductor nanowire photodetectors at the level of a single nanowire and then on an array of nanowire photodetectors. The biofunctionalization of this array and its hybridization with labelled DNA strands will be explored.

Apprenticeship Learning Platform deployment for industrial applications





This project aims at developing a demonstrator that integrates state-of-the-art technologies and improve it on a use-case representative of the industrial world. The demonstrator will consist in a robotic / cobotic arm coupled to an acquisition sensor (RGBD type). This device will be positioned in a workspace made of a rack / shelf containing objects / pieces of various shapes and qualities (materials, densities, colors ...) in front of which will be placed a typical conveyor prototype of industrial installations. The type of tasks expected to be carried out by the demonstrator will be "pick and place" type tasks where an object will have to be identified in shelf and then placed on the conveyor. This type of demonstrator will be closer to the real industrial conditions of use than the "toy" examples used in the academic field. This demonstrator will focus first on the short-term effectiveness based on state of the art technologies for both hardware and software, for a use case representative of the industrial world. At first, it will thus be less focused on the evolution of the algorithms used than on the adaptation of the parameters, the injection of knowledge a priori dependent on the context making it possible to reduce the high-dimensional input space, etc.

Global offshore wind turbines monitoring using low cost devices and simplified deployment methods





This project follows previous work focused on on-shore wind turbine instrumentation with inertial sensors networks whose dataflows allows the detection of vibration modes specific to the wind turbine components, in particular the mast and the real-time monitoring of these signals. The objectives of this project are manyfolds: to bring this work to offshore wind turbines; search for signatures in wider frequency bands; study the behavior of offshore platforms and their anchorages. One of the challenges is to find the signatures of rotating elements (blades) without direct instrumentation. Instrumentation of these elements is indeed more expensive and more impacting on the structure. In addition, the sensor technology will be suitable for monitoring the fatigue life cycle of moving wire structures (dynamic electrical connection cable and anchoring) in the case of an off-shore wind turbine. The ultimate goal is to propose a global method for offshore wind turbine health monitoring.

Shape optimization for optical computation

Département d'Optronique (LETI)

Laboratoire des Capteurs Optiques



Context CEA - LETI is one of the Europe's leading research centers in microelectronics. This post-doctoral position is proposed within the framework of the CLEAR CARNOT project, involving two departments of CEA - LETI. Namely, the DOPT department specializes in the design, manufacture and characterization of optoelectronic components while the DACLE department performs research on embedded systems and innovative computational architectures. Research topic / Missions Although several extremely compact components performing more or less complex unitary functions have been designed and manufactured in recent years, no practical application of numerical shape optimization methods has emerged to date in the field of integrated photonics. Building on the recent development of optical computation, particularly in the wake of neural network and machine learning, we aim to demonstrate the feasibility (design, fabrication and test) and the applicative interest of integrated optical calculation circuits obtained by shape optimization techniques. The candidate will participate in the choice of the optical computation prototype architecture, and will be in charge of the photonic circuit design (conventional circuit, shape optimized circuit, and finally reconfigurable circuit). He/she will rely on an existing toolbox, dedicated to shape optimization of photonic components, developed as part of an ongoing work. The work should lead to theoretical developments as well as applications, with publications in international journals. Required skills Candidates will have completed a PhD in applied mathematics, mathematical physics or related fields. He/she should demonstrate both theoretical and computational skills. Implementations will be performed in the MATLAB language. Knowledge in shape optimization and an interest for photonics would be greatly appreciated.

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