The Intelligent Vehicles section at the TU Delft, the Netherlands, invites applications for a fully funded Post-Doctoral research position in the area of Deep-Learning Based Radar Processing in Intelligent Vehicles. The intended research addresses problems within the spectrum of object detection, semantic scene analysis and vehicle localization. Apart from radar-based processing, data fusion with video is of interest. The position is funded by industry partner NXP.
We are seeking Post-Doc applicants with an interest in performing cutting edge research in an active and exciting research area (cf. self-driving cars by Google, Apple and the automotive industry). Prospective applicants should have a strong academic record with a solid background in sensor processing (vision/radar/LiDAR, sensor fusion) and Machine Learning (in particular: Deep Learning). Good programming skills are expected, preferably in C++/Python. Knowledge of deep-learning frameworks (TensorFlow/PyTorch/Keras/Caffe) and OpenCV/ROS/CUDA is a plus. A certain affinity towards turning complex concepts into real-world practice (i.e. vehicle demonstrator) is desired. The successful candidate is expected to be able to act independently as well as to collaborate effectively with members of a larger team. Good English skills are required.
The Post-Doctoral research appointment is full time (38 hours a week) and will be for an initial period of two years and possibly renewable for a third year. Salaries are in accordance with the university regulations for academic personnel. The monthly Post-Doc salary will range from €3044 to €3756 based on experience. The salary figures are before tax based on a full-time appointment. Secondary benefits amount to an additional 16.3% of yearly salary.
Living conditions in the Netherlands (e.g. Delft, Hague, Amsterdam) are considered to be among the very best in Europe. The TU Delft scores consistently high in international comparisons (e.g. within top 20 in QS World Univ. Rankings in Engineering and Technology).
Applications should be directed to Prof. D.M. Gavrila and submitted at the earliest convenience. They must include a motivation letter explaining why you are the right candidate, a CV, a transcript of graduate-level courses (M.S., Ph.D.), a link to your Ph.D. Thesis, a list of projects you have worked on with brief descriptions of your contributions (max 2 pages), a list of your publications and the names and contact addresses of two references. All these items should be combined in one PDF document and uploaded as “resume” below.