Future cars will incorporate ever more artificial intelligence (AI) to support driver safety, navigation, and comfort. Much of this AI will be embedded close to the sensors, i.e., at the edge, continuously updating perceptual models of the car's environment to enable driver-assist systems to function optimally and to detect potential safety treats.
A critical sensor modality for mobile perception is radar, and future cars will likely be equipped multiple imaging radars distributed across the surface of the car. Realizing the full potential of such rich sensing infrastructure for safety-critical applications in for autonomous driving requires advanced signal processing solutions that enable:
(1) Precise target detection and classification, and
(2) Super-resolution target localization and velocity estimation,
while navigating the ever more complex automotive environment challenged by cluttered scenes, many users, and hence many false detections.
Equipping many cars with such advanced radar systems is clearly an opportunity but it indeed also creates further challenges. Radar systems are active imaging systems, probing their environment by emitting coded electromagnetic waves. The signals generated by many such systems operating simultaneously requires new smart signal processing approaches and methods to deal with this interference.
AI will play a critical role in this processing pipeline, equipping its processors with the ability to explicitly incorporate expressive data-driven models of the scene, sensory radar observations, and derived perceptual inference.
To drive the development of AI-based radar signal processing chips, TU/e has teamed up with NXP, the globally leading Automotive IC design company, in a joint research program. Together, we aim to harvest the opportunities given by massive radar sensing and beyond-state-of-the-art AI methods, to develop new intelligent hybrid data- and model-based signal processing solutions that operate at the edge.
In this program PhD students and Postdoctoral researchers will be co-supervised by top AI experts from TU/e and leading radar signal processing experts from NXP. Students and researchers will also be hosted by NXP on a part-time basis and embedded in the AI for radar signal processing team, and the AI cluster of the signal processing systems group at TU/e. As a result, the positions in the program offer a unique combination of high-quality (fundamental) scientific and industrial experience.
Specifically, we are seeking candidates to help drive research projects exploring domain adaptation and unsupervised learning to relax the constraints and costs imposed by excessive annotation and data collection whenever sensors are newly developed, deployed in new environments, or sensor characteristics change over time. For these positions we are looking for talented, team-working-oriented and inquisitive candidates with an electrical engineering background and strong signal processing or AI skills. Applications from computer science and AI MSc students with affinity for signal processing, sensing and implementation are also welcomed.
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.
Do you recognize yourself in this profile and would you like to know more?
Please contact dr.ir. R.J.G.v.Sloun, r.j.g.v.sloun[at]tue.nl.
Are you inspired and would like to know more about working at TU/e? Please visit our career page.
We invite you to submit a complete application by using the apply button. The application should include a:
We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.
Please keep in mind you can upload only 5 documents up to 2 MB each. If necessary, please combine files.