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

PostDocs : selection by topics

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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.

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Bio-inspired approach for adversarial machine learning

Département Composants Silicium (LETI)

Laboratoire de Simulation et Modélisation



the target of the subject is to analyze a bio-inspired approach based on the so-called Catastrophic Forgetting paradigm to better understand the inherent mechanisms of adversarial attacks and propose new defense scheme against such integrity flaws of classical Machine Learning models (here, deep neural networks). Thus, the topic of the post-doctoral position gathers two major critical issues in the field of Machine Learning and more particularly for deep neural networks: - Catastrophic Forgetting (or Catastrophic Inference) is a phenomenon referring to the predisposition of a model to forget previously learned information when training with new one. More and more research efforts are focused on overcoming this critical behavior. Previous works lead in the DCOS department in Grenoble prove the relevance and efficiency of re-injections techniques to tackle the Catastrophic Forgetting issue for deep neural network. - Adversarial Examples refer to an integrity attack where an adversary try to tamper inputs at inference time to fool the decision of a model. This issue is now a popular ML topic with a very dynamic community but with still major open questions and a critical lack of robust defense strategies. The innovative idea of the project associated to this post-doctoral position is to use research from Neuroscience focused on ?Catastrophic Forgetting? to design and evaluate new defense strategies against adversarial examples. The main goal of the post-doctoral work will be to investigate the use of specific networks associated to reinjection processes, as developed in a human memory model and explore how the reinjection procedure use to avoid the catastrophic forgetting issue can alleviate the number of miss-classifications produced by adversarial attacks.

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Optical sensor development for in-situ and operando Li-ion battery monitoring

Département de l'Electricité et de l'Hydrogène pour les Transports (LITEN)

Laboratoire Analyse électrochimique et Post mortem



To improve the battery management system, it is required to have a better knowledge of the physical and chemical phenomena inside the cells. The next generation of cells will integrate sensors for deepest monitoring of the cell to improve the performances, safety, reliability and lifetime of the battery packs. The main challenge is thus to measure relevant physico-chemical parameters in the heart of the cell to get a direct access to the real state of the cell and thus to optimize its management. To address this challenge, a research project will start at CEA at the beginning of 2020 to develop innovative optical sensors for Li-ion battery monitoring. He / She will participate, in a first step, to the development of optical probes and their integration on optical fibres. The work will focus on the synthesis of a photo-chemical probe (nanoparticle and/or molecule) as active part of the sensor. Then, theses probes will be put on the optical fibre surface to form the sensor. The candidate will also participate to the realization of an optical bench dedicated to the testing of the sensors. In a second step, he / she will work on integrating the sensors into the Li-ion cells and test them in different conditions. The objective is to demonstrate the proof of concept: validation of the sensors efficiency to capture the behaviour of the cell and correlate it to electrochemical measurements.

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