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

PostDocs : selection by topics

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Compressed Sensing for ultrasonic imaging: disruptive method development and prototyping

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Méthodes CND

01-01-2020

PsD-DRT-19-0099

olivier.mesnil@cea.fr

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.

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Tools and methods for Industry 4.0 complex systems engineering

Département Ingénierie Logiciels et Systèmes (LIST)

Labo.conception des systèmes embarqués et autonomes

01-10-2020

PsD-DRT-20-0103

saadia.dhouib@cea.fr

The Fourth Industrial Revolution or Industry 4.0, is the current transformation of traditional manufacturing and industrial practices with the latest smart and digital technologies. Interoperability is the basis of Industry 4.0 and guarantees open and pluralistic markets. It enables systems, devices and applications to communicate with one another and work together seamlessly. Open standards are essential components for interoperability, the Asset Administration Shell (AAS) is a promising standard for ensuring interoperability and implementing the digital twin in the manufacturing domain. The AAS is being further developed in application-oriented research projects (German and French national projects, as well as European projects), tested in ten testbeds and implemented in (at least) two pilot projects. The goal of your postdoc will be to investigate methods and develop modular, compositional and interoperable software architectures and tools based on models and integrated with available standards (AAS, AutomationML, SenML, OPC-UA, CoaAP, MQTT) and software frameworks from the Industry 4.0 community, such as Basyx . Artificial intelligence plays a key role in terms of enabling assets to exchange data and information directly with one another and to make decisions autonomously. You will also investigate the design and implementation of AI components to arrive at new solutions and business models in the AAS based ecosystem. Together with senior members of the lab, you will get involved in EU projects such as CanvAAS and AI-REGIO. The main goal is to set-up and consolidate a vibrant ecosystem, tool-chain and community that will provide and integrate model-based design for Industry 4.0, digital twin implementation and liable AI solutions in the manufacturing domain.

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Post-doctoral position in AI safety and assurance at CEA LIST

Département Ingénierie Logiciels et Systèmes (LIST)

Labo.conception des systèmes embarqués et autonomes

01-10-2020

PsD-DRT-20-0110

morayo.adedjouma@cea.fr

The position is related to safety assessment and assurance of AI (Artificial Intelligence)-based systems that used machine-learning components during operation time for performing autonomy functions. Currently, for non-AI system, the safety is assessed prior to the system deployment and the safety assessment results are compiled into a safety case that remains valid through system life. For novel systems integrating AI components, particularly the self-learners systems, such engineering and assurance approach are not applicable as the system can exhibit new behavior in front of unknown situations during operation. The goal of the postdoc will be to define an engineering approach to perform accurate safety assessment of AI systems. A second objective is to define assurance case artefacts (claims, evidences, etc.) to obtain & preserve justified confidence in the safety of the system through its lifetime, particularly for AI system with operational learning. The approach will be implemented in an open-source framework that it will be evaluated on industry-relevant applications. The position holder will join a research and development team in a highly stimulating environment with unique opportunities to develop a strong technical and research portfolio. He will be required to collaborate with LSEA academic & industry partners, to contribute and manage national & EU projects, to prepare and submit scientific material for publication, to provide guidance to PhD students.

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Approximate Bayesian inference based on stochastic simulation

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Modélisation et Simulation des Systèmes

01-12-2020

PsD-DRT-20-0114

eric.barat@cea.fr

In many scientific fields, from particle physics to cosmology, including molecular biology and epidemiology, it is now common practice to develop simulation tools in order to describe complex phenomena. These simulation-based models are often stochastic (Monte Carlo) and have multiple input parameters. While the primary object of stochastic simulation is to be able to generate data from a configuration of parameters (forward simulation), its practical interest often resides in the opposite problem: determining a configuration of parameters of the model making it possible to generate data sufficiently close to those observed in Nature. Knowledge of these parameters can then represent the objective of the study or be used to calibrate the simulator for subsequent analyzes. However, solving such a nonlinear and very indirect problem is in general a difficult task. Our goal is to build a rigorous statistical inference framework for estimating these parameters. In particular, we propose to adopt the Bayesian paradigm for the resolution of the inverse problem in order to characterize the set of solutions via their a posteriori distribution. However, this objective comes up against a fundamental difficulty here: we do not have the analytical expression of likelihood in the context of stochastic simulation (likelihood free). This challenge has recently appeared to be amenable thanks to the emergence of two complementary techniques: ABC (Approximate Bayesian Computation) and deep generative models. As part of this project, we propose to evaluate the feasibility of this approach in an application scenario in the field of stochastic particle transport.

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Advanced tandem time of flight mass spectrometry for microelectronic applications

Département des Plateformes Technologiques (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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Scalable digital architecture for Qubits control in Quantum computer

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Intégration Silicium des Architectures Numériques

01-01-2021

PsD-DRT-20-0116

eric.guthmuller@cea.fr

Scaling Quantum Processing Units (QPU) to hundreds of Qubits leads to profound changes in the Qubits matrix control: this control will be split between its cryogenic part and its room temperature counterpart outside the cryostat. Multiple constraints coming from the cryostat (thermal or mechanical constraints for example) or coming from Qubits properties (number of Qubits, topology, fidelity, etc?) can affect architectural choices. Examples of these choices include Qubits control (digital/analog), instruction set, measurement storage, operation parallelism or communication between the different accelerator parts for example. This postdoctoral research will focused on defining a mid- (100 to 1,000 Qubits) and long-term (more than 10,000 Qubits) architecture of Qubits control at room temperature by starting from existing QPU middlewares (IBM QISKIT for example) and by taking into account specific constraints of the QPU developed at CEA-Leti using solid-state Qubits.

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