Measurement of active cell nematics by lensless microscopy

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 of the post-doc is to extend the analysis of the datasets produced by lens-free video microscopy. The post-doc will assist our partner in conducting the experimentations and will develop the necessary algorithms to reconstruct the images of the cell culture in different conditions. In particular, we will challenge the holographic reconstruction algorithms with the possibility to quantify the optical path difference (i.e. the refractive index multiplied by the thickness). Existing algorithms allow to quantify isolated cells. They will be further developed and assessed to quantify the formation of cell stacking in all three dimensions. These algorithms will have no Z-sectioning ability as e.g. confocal microscopy, only the optical path thickness will be measured.
We are looking people who have completed a PhD in image processing and/or deep learning with skills in the field of microscopy applied to biology.

Dynamic monitoring by light scattering of mass transfer between two phases in multiphase flows

The understanding and the modeling of recycling processes studied at CEA, require the measurement of both local and average properties of multiphase flows involved in chemical engineering devices. Moreover, as the R&D studies are generally conducted on small-scale experiments, access to these quantities is often difficult, especially considering that measurement methods should not disturb the observed system. In this context, optical methods, associated to extensive and rigorous physical simulations of light/particles interactions, are particularly relevant and, accordingly under specific developments since several years. Therefore, the DMRC/LGCI (CEA Marcoule), in collaboration with the laboratory IUSTI (CNRS and Aix-Marseille University), develops two optical interferometric techniques suitable for R&D studies: the Digital In-line Holography (DIH) and the Rainbow Refractometry (RR). Previous works have shown that DIH allows a simultaneous measurement of 3D-positions, shape and size of flowing particles, even considering astigmatic geometries, while RR gives access to the size and refractive index of each particle or of set of particles, which considering linear optics is directly linked to their composition. This study aims to go further in multiphase flows characterization with these two technics by following three main objectives: 1) propose original solutions for the characterization of materiel compositions thanks to DIH, 2) deepen inverse methods in RR to allow the study of clouds of particles with variable compositions and to take into account gradients of concentration around a sessile drop, 3) evaluate the relevance of these technics for lab on chip systems.

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

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.

Time-resolved in-situ study, by X-ray diffraction under synchrotron radiation, of structural evolutions in a high temperature oxidized zirconium alloys

In certain hypothetical accident situations in pressurized-water nuclear reactors (PWRs), the zirconium alloy cladding of fuel pallets, which constitutes the first barrier for the containment of radioactive products, can be exposed for a few minutes to water vapor. at high temperature (up to 1200 ° C), before being cooled and then quenched with water. The cladding material then undergoes numerous structural and metallurgical evolutions. In order to study these structural evolutions in a precise way, a first experiment campaign was carried out on the BM02 line of the ESRF on a prototype furnace allowing to perfectly control the atmosphere and the temperature. Two tasks will be entrusted to the candidate: continue and finish the analysis of the first experiment(phase fraction determination, residual constraints ...) and prepare a new complementary experimental proposal by mid 2020.

IMPROVING OPTICALLY PUMPED MAGNETOMETERS FOR BIOMEDICAL IMAGING

Our lab works on optically pumped magnetometers (OPM) based on helium-4 metastable atoms. Our main achievement in last years has been the design and space qualification of the most advanced OPMs available for spatial exploration, which were launched on ESA Swarm mission [1]. With this very same species we have developed OPMs for medical imaging of brain (MEG) and heart (MCG), which have the advantage of operating at room temperature. The development of these two imaging techniques is an opportunity to better understand and diagnose pathologies like epilepsy, Alzheimer or arrhythmia.
A few years ago we performed proof of concept measurements of both MCG and MEG with primitive versions of our sensors [2,3]. After getting a better understanding of our sensors physics [4] and implementing substantial improvements, we are now developing arrays of OPMs and collaborating with several clinical teams in order to test them for different applications and environments. The purpose of this post-doctoral position is to contribute to the development of magnetometer arrays. It involves experimental work to improve the current prototypes of medical OPM arrays: the post-doc will be notably in charge of improving the intrinsic noise of the sensor and identifying the best way to build robust, reproducible architectures that could be replicated in arrays of several hundreds of sensors.
This work is aimed at bringing this technology to the medical imaging market, in collaboration with a start-up currently prepared by CEA-Leti. It will be carried out in a multidisciplinary team, composed of researchers, experienced engineers, PhD students and post-docs, specialized in the fields of optics, lasers, magnetism and electronics. It will also rely on collaborations with medical research teams in neurology and cardiology.

[1] http://smsc.cnes.fr/SWARM
[2] S. Morales et al.,
[3] E. Labyt et al., IEEE Transactions on Medical Imaging (2019).
[4] F. Beato et al. Physical Review A (2018)

LAB AND FIELD WORK ON OPTICALLY PUMPED MAGNETOMETERS

The purpose of this post-doctoral position is to contribute to the development of magnetometer arrays. It involves mainly the deployment of OPM arrays in the clinical environments where they are going to be tested by several of our partner medical research teams in both neurology and cardiology. The post-doc should be able to deploy and operate the sensors in these environments, solve the practical issues, and bring feedback on all kind of improvements that are needed. He or she will also participate in the implementation of some of these improvements, and their tests in lab environment.
This work is aimed at bringing this technology to the medical imaging market. It will be carried out in a multidisciplinary team, composed of researchers and experienced engineers.

[1] http://smsc.cnes.fr/SWARM
[2] S. Morales et al., Phys. Med. B
[3] E. Labyt et al., IEEE Transactions on Medical Imaging (2019).
[4] F. Beato et al. Physical Review A (2018)

Application of a MDE approach to AI-based planning for robotic and autonomous systems

The complexity of robotics and autonomous systems (RAS) can only be managed with well-designed software architectures and integrated tool chains that support the entire development process. Model-driven engineering (MDE) is an approach that allows RAS developers to shift their focus from implementation to the domain knowledge space and to promote efficiency, flexibility and separation of concerns for different development stakeholders. One key goal of MDE approaches is to be integrated with available development infrastructures from the RAS community, such as ROS middleware, ROSPlan for task planning, BehaviorTree.CPP for execution and monitoring of robotics tasks and Gazebo for simulation.
The goal of this post-doc is to investigate and develop modular, compositional and predictable software architectures and interoperable design tools based on models, rather than code-centric approaches. The work must be performed in the context of European projects such as RobMoSys (www.robmosys.eu), and other initiatives on AI-based task planning and task execution for robotics and autonomous systems. The main industrial goal is to simplify the effort of RAS engineers and thus allowing the development of more advanced, more complex autonomous systems at an affordable cost. In order to do so, the postdoctoral fellow will contribute to set-up and consolidate a vibrant ecosystem, tool-chain and community that will provide and integrate model-based design, planning and simulation, safety assessment and formal validation and verification capabilities.

Simulation and electrical characterization of an innovative logic/memory CUBE for In-Memory-Computing

For integrated circuits to be able to leverage the future “data deluge” coming from the cloud and cyber-physical systems, the historical scaling of Complementary-Metal-Oxide-Semiconductor (CMOS) devices is no longer the corner stone. At system-level, computing performance is now strongly power-limited and the main part of this power budget is consumed by data transfers between logic and memory circuit blocks in widespread Von-Neumann design architectures. An emerging computing paradigm solution overcoming this “memory wall” consists in processing the information in-situ, owing to In-Memory-Computing (IMC).
However, today’s existing memory technologies are ineffective to In-Memory compute billions of data items. Things will change with the emergence of three key enabling technologies, under development at CEA-LETI: non-volatile resistive memory, new energy-efficient nanowire transistors and 3D-monolithic integration. At LETI, we will leverage the aforementioned emerging technologies towards a functionality-enhanced system with a tight entangling of logic and memory.
The post-doc will perform electrical characterizations of CMOS transistors and Resistive RAMs in order to calibrate models and run TCAD/spice simulations to drive the technology developments and enable the circuit designs.

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.