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

PostDocs : selection by topics

Direct interfacing of bio-inspired NEMS Sensors to bio-inspired RRAM spikingnetworkRKS

Département Composants Silicium (LETI)

Laboratoire de Composants Mémoires



Extracting useful and compact information from sensor data is key for future mobile and Internet of Things (IoT) applications. Mining data from raw sensors remains an open problem, so that systems capable of handling large volumes of noisy and incomplete real-life data are required. Today, the most promising approach is deep learning. Despite its benefit, the adoption of deep learning within IoT faces significant barriers due to the constraints imposed by mobile devises (memory, power consumption, and limited transmission range). One possible approach to tackle these challenges is to rethink and reorganize computer architecture taking inspiration of living organisms. Insects are not able to perform calculations like digital systems but excel in controlling small and agile motor systems based on the fusion of data sparse sensory inputs. Moreover, they operate under severe constrains, of energy conservation and limited communication range, among others. Therefore, they provide highly interesting model systems for neuromorphic embedded computation. Resistive RAM (RRAM) are non-volatile memory elements whose values/conductances change as a function of the applied pulses. Thanks to these properties they are prime candidates for implementing plastic synapses in neuromorphic systems. Arrays of micromechanical pillars mimicking the cricket hairs have been demonstrated to be excellent air flow sensors. The main objective of the project is to develop a bio-inspired RRAM-based spiking neural network directly interfaced with a bio-inspired MEMS sensor for readout and local information processing. The main research objective is the design, fabrication and test of a RRAM-based spiking neural network for the readout of an already available nanomechanical resonator array. The alleged advantages of the proposed bio-inspired design throughout the whole system will be demonstrated by simulations calibrated on the experimental results.

Microfluidics for a human Pancreas-on-a chip

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Biologie et Architecture Microfluidiques



The context of the project concerns the microsystems for biology whose stakes are the integration and the automation of protocols assays for healthcare applications. In this field there is a growing interest on the development of organs on chips. An organ-on-a-chip is a microfluidic cell culture device created with micro-manufacturing technics. The fluidics networks create the mimicking in vivo environment for cell culture such as perfusions and physiologic stimulation, thus making it possible to have biological models more relevant than those currently available. The goal is to engineer a microfluidic platform that can recapitulate functional units of human pancreas.

Application of ontology and knowledge engineering to complex system engineering

Département Ingénierie Logiciels et Systèmes (LIST)

Labo. ingénierie des langages exécutables et optimisation



Model-Based System Engineering relies on using various formal descriptions of the system to make prediction, analysis, automation, simulation... However, these descriptions are mostly distributed across heterogeneous silos. The analysis and exploitation of the information are confined to their silos and thereby miss the big picture. The crosscutting insights remain hidden. To overcome this problem, ontologies and knowledge engineering techniques provide desirable solutions that have been acknowledged by academic works. These techniques and paradigm notably help in giving access to a complete digital twin of the system thanks to their federation capabilities, in making sense to the information by embedding it with existing formal knowledge and in exploring and uncovering inconsistencies thanks to reasoning capabilities. The objective of this work will be to propose an approach that gives access to a complete digital twin federated with knowledge engineering technologies. The opportunities and limits of the approach will be evaluated on industrial use cases.

Development of a cell analysis algorithm for phase microscopy imaging

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Imagerie et Systèmes d'Acquisition



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.

Systemic Optimisation and Functional Digital Twin

Département Ingénierie Logiciels et Systèmes (LIST)

Labo. ingénierie des langages exécutables et optimisation



The current economic constraints in the industrial field are getting tighter, which leads to increased competitiveness and a need to produce better and quicker. The optimisation of production processes and their design therefore lies at the centre of the considerations on the Factory of the Future. Optimisation needs are large and cover various scopes ranging from design and logistics to processes, with the objective of reducing time and costs while maintaining or even increasing the quality and tailoring of products and services. Optimisation and simulation tools need a comprehensive vision of the systems they study, which may be provided by a Functional Digital Twin of the factory/construction site. The approach of Model-Driven Engineering (MDE) allows engineers to design such a Twin and to interconnect it with numerical models (equations, 3D models ?), which allows validating and/or optimising the overall system operation through a complete Digital Twin. The goal of this Post-Doc is to investigate and develop a generic and configurable framework for process optimisation (scheduling, sizing ...) around MDE tool Papyrus and its simulator. An executable language, dedicated to the description of Digital Twins, has been implemented in Papyrus, and first industrial optimization projects have been completed. The main objective of this Post-Doc is to propose a generic simulation-based framework to solve optimisation problems of the factory/construction site. The goal is also to improve the decision support environment existing in Papyrus, using results of optimisations and simulations. The candidate will have to ensure a technology watch on the topics of process optimisation within the framework of the industry of the future and to organise and animate the topic of optimisation in the laboratory.

Apprenticeship Learning Platform deployment for industrial applications





This project aims at developing a demonstrator that integrates state-of-the-art technologies and improve it on a use-case representative of the industrial world. The demonstrator will consist in a robotic / cobotic arm coupled to an acquisition sensor (RGBD type). This device will be positioned in a workspace made of a rack / shelf containing objects / pieces of various shapes and qualities (materials, densities, colors ...) in front of which will be placed a typical conveyor prototype of industrial installations. The type of tasks expected to be carried out by the demonstrator will be "pick and place" type tasks where an object will have to be identified in shelf and then placed on the conveyor. This type of demonstrator will be closer to the real industrial conditions of use than the "toy" examples used in the academic field. This demonstrator will focus first on the short-term effectiveness based on state of the art technologies for both hardware and software, for a use case representative of the industrial world. At first, it will thus be less focused on the evolution of the algorithms used than on the adaptation of the parameters, the injection of knowledge a priori dependent on the context making it possible to reduce the high-dimensional input space, etc.

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