Scientific direction Development of key enabling technologies
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PostDocs : selection by topics

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Formalization of the area of responsibility of the actors of the electricity market

DSUD (CTReg)

Autre DPACA

01-06-2020

PsD-DRT-20-0074

bruno.robisson@cea.fr

The CEA is currently developing a simulation tool which models the energy exchanges between the actors of the electricity market but which models, in addition, the exchanges of information between those actors. The first results of this work show that, for some new energy exchange schemes, 'indirect' interactions between actors may appear and may cause financial damage (for example, the failure of a source of production of one actor may impact the income of another). Thus, the borders which clearly delimited until now the areas of responsibility of each actors could be brought to blur and their areas of responsibility could "overla". The candidate will be responsible for: - Formally define the area of responsibility of an actor in the electricity market, - Model the interactions, including 'indirect' ones, that may appear between these actors, - Apply formal proof techniques (such as 'model-checking') to detect overlaps in areas of responsibility, - Define the conditions of exchange between the actors which would guarantee the non-recovery of the areas of responsibility.

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High precision robotic manipulation with reinforcement learning and Sim2Real

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire Vision et Apprentissage pour l'analyse de scènes

01-09-2020

PsD-DRT-20-0082

jaonary.rabarisoa@cea.fr

High precision robotic assembly that handles high product variability is a key part of an agile and a flexible manufacturing automation system. To date however, most of the existing systems are difficult to scale with product variability since they need precise models of the environment dynamics in order to be efficient. This information is not always easy to get. Reinforcement learning based methods can be of interest in this situation. They do not rely on the environment dynamics and only need sample data from the system to learn a new manipulation skill. The main caveat is the efficiency of the data generation process. In this post-doc, we propose to investigate the use of reinforcement learning based algorithms to solve high precision robotic assembly tasks. To handle the problem of sample generation we leverage the use of simulators and adopt a sim2real approach. The goal is to build a system than can solve tasks such as those proposed in the World Robot Challenge and tasks that the CEA's industrial partners will provide.

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Fibre optic sensor instrumentation for thermomechanical measurements in harsh environment

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs Fibres Optiques

01-07-2021

PsD-DRT-21-0085

cyril.lefeuve@cea.fr

In connection with the people in charge of the project in the LCFO laboratory (DRT Saclay) and the LMES laboratory (DAM Cesta), the postdoctoral fellow will participate in the development of the on-board interrogation system, from both a system and an algorithmic point of view, as well as in the development of the fibre optic sensor test on the SPRITE installation. The person recruited will also be in contact with the LRP laboratory (DAM Le Ripault) in order to participate in tests of fibre integration within plasma-sprayed ceramics. He or she will carry out measurements of the spectral quality of the integrated sensor and will analyse these spectra once the integration has been carried out but also during the integration. The person recruited will be based mainly on the Saclay site but will be required to travel for a few days to the CEA Cesta and CEA Le Ripault sites in order to set up and carry out the experiments. Skills in optics, instrumentation, embedded systems and algorithms are required.

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Design and control of a multimodal multi-fingered gripper for force and vision-guided robotic manipulation

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire d'Architecture des Systèmes Robotiques

01-01-2021

PsD-DRT-20-0096

mathieu.grossard@cea.fr

The current offer focuses on the design and control approaches of functionally integrated multi-fingered grippers to be used for force- and vision-guided robotic manipulation tasks. The to-be-designed grasping tool will take advantage of multimodal sensing capabilities (embedding tactile sensors, mounted in a meaningful way within the mechatronic device, together with a self-sensing actuation apparatus) combined with a vision system to achieve autonomously fine force-controlled manipulation tasks (such as dexterous in-hand manipulation, pick-and place of rigid and flexible objects, component assembly, etc.). To do so, the previously designed multimodal system will be exploited for learning-by-demonstration tasks (relying on Human/Robot co-manipulation phase) to build a learnt database combining tactile, force and vision cues for a series of objects with various characteristics (in terms of geometry, shape, texture, mass, etc.). This database would first cover known objects, before being extended to objects in various poses and then to unknown objects using learning-based strategies.

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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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