TU Delft

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Originele vacaturetekst

Postdoc Deep-Learning Based Radar Processing for Intelligent Vehicles

Faculty Mechanical, Maritime and Materials Engineering

The Faculty of 3mE carries out pioneering research, leading to new fundamental insights and challenging applications in the field of mechanical engineering. From large-scale energy storage, medical instruments, control technology and robotics to smart materials, nanoscale structures and autonomous ships. The foundations and results of this research are reflected in outstanding, contemporary education, inspiring students and PhD candidates to become socially engaged and responsible engineers and scientists. The faculty of 3mE is a dynamic and innovative faculty with an international scope and high-tech lab facilities. Research and education focus on the design, manufacture, application and modification of products, materials, processes and mechanical devices, contributing to the development and growth of a sustainable society, as well as prosperity and welfare.

Click here to go to the website of the Faculty of Mechanical, Maritime and Materials Engineering.

Functie omschrijving

The Intelligent Vehicles group 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.

Department
For more information about the Intelligent Vehicles group at TU Delft, see http://intelligent-vehicles.org.

Functie eisen

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.

Arbeidsvoorwaarden

TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities (salary indication: €3389-€4274 per month).
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).

Informeren en solliciteren

For additional information regarding this vacancy, please contact Prof. Dariu Gavrila, head Intelligent Vehicles group, see http://intelligent-vehicles.org.

Applications should 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. Please submit this document at the earliest convenience to Dariu Gavrila, Head Intelligent Vehicles group, application-3mE@tudelft.nl. When applying for this position, please refer to vacancy number TUD00197.

A pre-employment screening can be part of the application procedure.

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