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

PhD : selection by topics

Technological challenges >> Cyber physical systems - sensors and actuators
2 proposition(s).

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Innovative flexible piezoelectric sensors for tactile and acoustic interface ? Multifunction sensors

Département Systèmes (LETI)

Laboratoire Autonomie et Intégration des Capteurs



Cyber physical systems - sensors and actuators (.pdf)

The aim of the PhD thesis is to implement a matrix of flexible piezoelectric nanosensors, which enable the 3D reconstruction of a force or deformation field. The nanosensors based on GaN nanowires obtained by directed growth are fabricated and assembled at CEA. The candidate will tackle experimental aspects, which include the fabrication and the assembly of sensors and sensor networks (matrix) via controlled growth and deposition processes, first-level flexible electronic layers (interconnects), system integration on an object (mechatronics) and finally signal collection and processing through a dedicated reading electronics, to be designed based on the competences present in our laboratory. In parallel, the candidate will carry out studies at the fundamental level, such as investigating the mechanical transfer between the nanowire and its environment and its effect on the generated signal under deformation, or the study of the piezoelectric / pyroelectric coupling intrinsic to GaN nanowires. For this purpose, the candidate will have access to multi-physics simulation tools. Finally, investigations on the choice of materials and the characterisation thereof (structural, mechanical, thermal, optical, electrical) will be pertinent and may pursued. More generally, this PhD thesis will also provide the opportunity to develop applicable solutions in various fields such as deformation and impacts detectors for predictive maintenance, sensitive surfaces or electronic skin.

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Trajectory Prediction for Autonomous Navigation

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Intelligence Intégrée Multi-capteurs



Cyber physical systems - sensors and actuators (.pdf)

With the growing interest in Autonomous Vehicles (AV), perception systems play a central role in their navigation, with active developments from the research and automotive industry communities. Perception systems provide AVs with information about the driving situation. Basically, advanced algorithms model the vehicle environment using a map by processing past and present data from on-board sensors such as cameras, LiDARs, radars and ultrasounds. The future evolution of the driving environment is predicted in order to plan safe trajectory, avoid collisions and make navigational decisions. CEA has developed a patented on-board sensor fusion technology that exploits the occupancy grid paradigm to model the vehicle environment. This grid provides a probabilistic estimate of occupied and free regions. The estimation of obstacle movement is also under development. However, a prediction layer that estimates the likely future trajectories of moving obstacles is still missing. The objective of the PhD thesis is to develop an embedded trajectory prediction algorithm for autonomous navigation. Trajectory prediction is a spatio-temporal (4D) problem where uncertainty is essential to evaluate the probable short-term evolution of a driving scenario. The diversity of moving obstacles makes trajectory prediction very difficult when integrated within lightweight computing platforms. In fact, a moving car does not have the same degree of freedom as a pedestrian. Prediction models can take into account the nature of moving obstacles if this information is available (for example, provided by artificial intelligence). Otherwise, prediction models must adapt to the available data. During the thesis, the PhD student will first focus on the probabilistic modeling of motion and trajectory. Then, he/she will propose a low-complexity algorithmic solution that can run in real time on an embedded computing platform. The PhD student will be hosted in a team whose expertise is the development of advanced and lightweight perception solutions that can be integrated into embedded systems. The PhD student will collaborate with researchers, engineers and other PhD students from various scientific fields. The candidate must have a strong mathematical background in probability/statistics, computer science and software prototyping (matlab/python, C++). Knowledge and skills in artificial intelligence and data fusion will be a plus.

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