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PostDocs : selection by topics

Advanced tandem time of flight mass spectrometry for microelectronic applications

Département technologies silicium (LETI)

Autre laboratoire

01-01-2020

PsD-DRT-19-0115

jean-paul.barnes@cea.fr

The CEA LETI seeks to recruit a post-doctoral researcher to work on the development of advanced time of flight secondary ion mass spectrometry applications (TOF-SIMS). The candidate will work on a new TOF-SIMS instrument equipped with tandem MS spectrometry, in-situ FIB and Argon cluster sputtering. The research project will be focused around the following topics ? Developing methods to correlate TOF-SIMS with AFM, XPS and Auger ? Improving the sensitivity and efficiency of fragmention of the tandem MS spectrometer ? Developing 3D FIB-TOF-SIMS applications and improving the spatial resolution. The candidate will also have access to the wide range of state of the art instruments present on the nanocharacterisation platform as well as bespoke samples coming from the advanced technology branches developed at the LETI. The candidate will also benefit from a collaboration with the instrument supplier.

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Bio-inspired approach for adversarial machine learning

Département Composants Silicium (LETI)

Laboratoire de Simulation et Modélisation

01-10-2019

PsD-DRT-19-0117

marina.reyboz@cea.fr

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

01-01-2020

PsD-DRT-19-0121

olivier.raccurt@cea.fr

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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