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

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New carbon materials for water-analysis sensors

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Chimie, Capteurs et Biomatériaux



Technologies pour la santé et l'environnement, dispositifs médicaux (.pdf)

Electrochemical sensors are commonly used for water analysis because of their sensitivity, their versatility, and their relative simplicity of implementation at the instrumental level. However, their large-scale deployment for continuous monitoring of water resources as well as discharges resulting from agricultural, industrial or residential activities is severely limited by the lifetime of the sensors. This is directly related to the durability of the materials used but also to the fouling of the electrodes during immersion. Currently, diamond electrodes as developed at CEA LIST can overcome these technical limitations (chemical inertia, regeneration of the measurement interface, etc.) but require a manufacturing process that is far too costly for the vast majority of the applications identified. Amorphous carbon (known under the terminology "DLC" for "Diamond Like Carbon") seems to have similar technical characteristics to diamond, for a manufacturing cost 10 to 20 times less. The aim of this project is to demonstrate the interest of this new electrode material for water analysis in the field, to increase its analytical capacities (selectivity, sensitivity) by adding inorganic catalysts and to define a low-cost production process. Symbolically - but in line with user demands - the aim is to achieve a maintenance-free service life of one year for an autonomous system for monitoring drinking water distribution networks. The performances will be validated on a reduced set of representative sensors, for which there is a high demand (pH, free chlorine, nitrate) or which are of strong societal interest (chlorophenols, used in many pesticides).

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Measurement of active cell nematics by lensless microscopy

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Systèmes d'Imagerie pour le Vivant



Technologies pour la santé et l'environnement, dispositifs médicaux (.pdf)

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.

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

Département Ingénierie Logiciels et Systèmes (LIST)

Labo.conception des systèmes embarqués et autonomes



Data intelligence dont Intelligence Artificielle (.pdf)

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 (, 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.

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Non-volatile asynchronous magnetic SRAM design

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Intégration Silicium des Architectures Numériques



Systèmes cyberphysiques - capteurs et actionneurs (.pdf)

In the applicative context of sensor nodes as in Internet of things (IoT) and for Cyber Physical Systems (CPS), normally-off systems are mainly in a sleeping state while waiting events such as timer alarms, sensor threshold crossing, RF or also energetic environment variations to wake up. To reduce power consumption or due to missing energy, the system may power off most of its components while sleeping. To maintain coherent information in memory, we aim at developing an embedded non-volatile memory component. Magnetic technologies are promising candidates to reach both low power consumption and high speed. Moreover, due to transient behavior, switching from sleeping to running state back and forth, asynchronous logic is a natural candidate for digital logic implementation. The position is thus targeting the design of an asynchronous magnetic SRAM in a 28nm technology process. The memory component will be developed down to layout view in order to precisely characterize power and timing performances and allow integration with an asynchronous processor. Designing such a component beyond current state of the art will allow substantial breakthrough in the field of autonomous systems.

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





Réseaux énergétiques intelligents (.pdf)

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



Usine du futur dont robotique et contrôle non destructif (.pdf)

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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