Artificial Intelligence applied to Ion Beam Analysis
A one year contract postdoctoral research position is open at the laboratory for light element studies (LEEL, CEA/DRF) and the Data Science for Decision Laboratory (LS2D, DRT/LIST) and focuses on data processing based on AI and machine learning, here in the scope of Ion Beam Analysis (IBA).
In the context of this project, the successful candidate will have to fulfill the following tasks:
1- Design of a multispectral dictionary.
2- Learning module development.
3- Main code programming.
4- Development of a module dedicated to multispectral mappings.
5- Benchmarking.
The postdoctoral research associate will be hosted and supervised within LEEL and LS2D.
Detection of cyber-attacks in a smart multi-sensor embedded system for soil monitoring
The post-doc is concerned with the application of machine learning methods to detect potential cyber-security attacks on a connected multi-sensor system. The application domain is the agriculture, where CEA Leti has several projects, among which the H2020 project SARMENTI (Smart multi-sensor embedded and secure system for soil nutrient and gaseous emission monitoring). The objective of SARMENTI is to develop and validate a secure, low power multisensor systems connected to the cloud to make in situ soil nutrients analysis and to provide decision support to the farmers by monitoring soil fertility in real-time. Within this topic, the postdoc is concerned with the cyber-security analysis to determine main risks in our multi-sensor case and with the investigation of a attack detection module. The underlying detection algorithm will be based on anomaly detection, e.g., one-class classifier. The work has tree parts, implement the probes that monitor selected events, the communication infrastructure that connects the probes with the detector, and the detector itself.
Nanofabrication of spintronic spiking neurons
In the frame of the French national ANR project SpinSpike, Spintec laboratory is opening a postdoctoral researcher position. The candidate will work in collaboration with UMPhy CNRS-Thales and Thales TRT. The objective is the realization of proof-of-concept magnetic tunnel junction based artificial spiking neurons able to generate spikes and propagate them between coupled artificial neurons.
The candidate should have a strong background in nanofabrication and should be familiar with common techniques of optical and e-beam lithography as well as different etching techniques. The candidate can also be involved in the electrical characterization of the devices.
The position is expected to start on April 1, 2021 and go on for up to 2 years jointly between the RF team and MRAM teams of Spintec. The contract will be managed by CEA and funded by ANR Agency.
We offer an international and competitive environment, state-of-the-art equipment, and the possibility to perform research at the highest level. We promote teamwork in a diverse and inclusive environment and welcome all kinds of applicants. Further information about Spintec laboratory www.spintec.fr .
Compressed Sensing Electron Tomography: Quantitative Multi-dimensional Characterization of Nanomaterials
Electron tomography (ET) is a well-established technique for the 3D morphological characterization at the nanoscale. ET applied to spectroscopic modes for 3D structural and chemical analysis has become a hot topic but necessitates long exposure times and high beam currents. In this project, we will explore advanced compressed sensing (CS) approaches in order to improve the resolution of spectroscopic ET and reduce significantly the dose. More precisely, we will focus on the following two tasks: 1. Comparison of total variation minimization, orthogonal or undecimated wavelets, 3D curvelets or ridgelets and shearlets for nano-objects with different structures/textures; 2. Comparison of PCA and novel CS-inspired methods such as sparse PCA for dimensionality reduction and spectral un-mixing. The code will be written in Python, using Hyperspy (hyperspy.org) and PySAP (https://github.com/CEA-COSMIC/pysap) libraries.
The project follows a multidisciplinary approach that involves the strong expertise of the coordinator in ET and the input of two collaborators with complementary skills: Philippe Ciuciu with expertise in MRI (DRF/Joliot/NEUROSPIN/Parietal) and Jean-Luc Starck with expertise in cosmology, signal processing and applied maths (DRF/IRFU/DAP/CosmoStat). The three communities share a strong interest in compressed sensing algorithms.
Data science for heterogeneous materials
In order to predict the functional properties of heterogeneous materials through numerical simulation, reliable data on the spatial arrangement and properties of the constitutive phases is needed. A variety of experimental tools is commonly used at the laboratory to characterize spatially the physical and chemical properties of materials, generating "hyperspectral" datasets. A path to progress towards an improved undestanding of phenomena is the combination of the various imaging techniques using the methods of data science. The objectives of this post-doc is to enrich material knowledge by developping tools to discover correlations in the datasets (for exemple between chemical composition and mechanical behavior), and to increase reliability and confidence in this data by combining techniques and physical constraints. These tools will be applied to datasets of interest regarding cementitious materials and corrosion product layers from archaeological artifacts.
Study of a transient regime of helium dispersion to simulate an accidental release of hydrogen from a fuel cell.
CEA and industrial partners want to improve their knowledge, models and risk mitigation means for the conséquences of an accidental release of hydrogen from a H2 Fuel Cell. The dispersion of helium as a replacement for hydrogen takes place in a private garage and the transient state will be studied. Different scenarios of release are considered: from a cubic idealized fuel cell, then with different aspect ratios and finally with varying main dimension. The goal is to study some scaling effects. For the first case, we will measure helium concentration with katarometres and possibly velocity fields with PIV methods. Then mitigation processes will be tested. At last comparisons with models and numerical simulations will be performed.
Development of a cell analysis algorithm for phase microscopy imaging
At CEA-Leti we have validated a video-lens-free microscopy platform by performing thousands of hours of real-time imaging observing varied cell types and culture conditions (e.g.: primary cells, human stem cells, fibroblasts, endothelial cells, epithelial cells, 2D/3D cell culture, etc.). And we have developed different algorithms to study major cell functions, i.e. cell adhesion and spreading, cell division, cell division orientation, and cell death.
The research project is to extend the analysis of the datasets produced by lens-free video microscopy. The objective is to study a real-time cell tracking algorithm to follow every single cell and to plot different cell fate events as a function of time. To this aim, researches will be carried on segmentation and tracking algorithms that should outperform today’s state-of-the-art methodology in the field. In particular, the algorithms should yield good performances in terms of biological measures and practical usability. This will allow us to outperform today’s state-of-the-art methodology which are optimized for the intrinsic performances of the cell tracking and cell segmentation algorithms but fails at extracting important biological features (cell cycle duration, cell lineages, etc.). To this aim the recruited person should be able to develop a method that either take prior information into account using learning strategies (single vector machine, deep learning, etc.) or analyze cells in a global spatiotemporal video. We are looking people who have completed a PhD in image processing, with skills in the field of microscopy applied to biology.
FDSOI technology scaling beyond 10nm node
FDSOI (Fully-Depleted Silicon On Insulator) is acknowledged as a promising technology to meet the requirements of emerging mobile, Internet Of Things (IOT), and RF applications for scaled technological nodes [1]. Leti is a pioneer in FDSOI technology, enabling innovative solutions to support industrial partners.
Scaling of FDSOI technology beyond 10nm node offers solid perspectives in terms of SoC and RF technologies improvement. Though from a technological point of view, it becomes challenging because of thin channel thickness scaling limitation around 5nm to maintain both good mobility and variability. Thus, introduction of innovative technological boosters such as strain modules, alternative gate process, parasitics optimization, according to design rules and applications, become mandatory [2].
The viability of these new concepts should be validated first by TCAD simulations and then implemented on our 300mm FDSOI platform.
This subject is in line with the recent LETI strategy announcement and investments to develop new technological prototypes for innovative technology beyond 28nm [3].
The candidate will be in charge to perform TCAD simulations, to define experiment and to manage them until the electrical characterization. The TCAD simulations will be performed in close collaboration with the TCAD team. The integration will be done in the LETI clean room in collaboration with the process and integration team. Candidate with out-of-the-BOX thinking, autonomy, and ability to work in team is mandatory.
[1] 22nm FDSOI technology for emerging mobile, Internet-of-Things, and RF applications, R. Carter et al, IEEE IEDM 2016.
[2] UTBB FDSOI scaling enablers for the 10nm node, L. Grenouillet et al, IEEE S3S 2013.
[3]https://www.usinenouvelle.com/article/le-leti-investit-120-millions-d-euros-dans-sa-salle-blanche-pour-preparer-les-prochaines-innovations-dans-les-puces.
Analysis of low abundance 144Ce and 106Ru isotopes by mass spectrometry
The aim of this project is to develop the high precision analysis of 144Ce and 106Ru by mass spectrometry in irradiated samples for the qualification of neutronic calculation codes. These two isotopes are present at low abundances in the samples of interest and display significant isobaric interferences with 144Nd and 106Pd respectively. To complete this project, the candidate will carry out the appropriate analytical developments in conventional laboratory on inactive samples. Then the procedure will be transposed in the active laboratory for validation with the analysis of real samples. In the case of 144Ce, the implementation of a coupling between high performance liquid chromatography (HPLC) and ICPMS-MC, combined with the isotope dilution technique for the precise determination of atomic contents is envisaged. For 106Ru, the 101Ru concentration will first be determined by ICPMS-Q and the 101Ru/106Ru ratio will be determined by HPLC/ICPMS-Q or HPLC/ICPMS-MC coupling to remove the 106Pd/106Ru interference.
Nano-silicon/graphene composites for high energy density lithium-ion batteries
This postdoctoral fellowship is part of the Graphene Flagship Core 2 H2020 european project (2018-2020) on the energy storage applications of graphene. In lithium-ion batteries, graphene associated to nanostructured silicon in a proper composite helps increase the energy capacity. Indeed graphene wraps silicon, reducing its reactivity with electrolyte and the formation of the SEI passivation layer. It also maintains a high electrical conductivity within the electrode.
The study will compare two technologies: graphene-silicon nanoparticles and graphene-silicon nanowires. The former composite, already explored in the above mentioned project, will be optimized in the present study. The latter is a new kind of composite, using a large scale silicon nanowire synthesis process recently patented in the lab. The postdoc will work within two laboratories: a technological research lab (LITEN) with expertise in batteries for transportation, and a fundamental research lab (INAC) with expertise in nanomaterial synthesis.
The postdoc will synthesize silicon nanowires for his/her composites at INAC. Following LITEN know-how, she/he will be in charge of composite formulation, battery fabrication and electrochemical cycling. He/she will systematically compare the electrochemical behavior of the nanoparticle and nanowire based silicon-graphene composites. Comparison will extend to the mechanism of capacity fading and SEI formation, thanks to the characterization means available at CEA Grenoble and in the European consortium: X-ray diffraction, electronic microscopy, XPS, FTIR, NMR spectroscopies. She/he will report her/his work within the international consortium (Cambride UK, Genova Italy, Graz Austria) meetings.
A 2-year post-doctoral position is open.
PhD in materials science is requested. Experience in nanocharacterization, nanochemistry and/or electrochemistry is welcome.
Applications are expected before May 31st, 2018.