Description: The IV group at the TU Delft invites applications for a fully a funded Ph.D. Student/Post-Doc position in Computer Vision (CV), Sensor Fusion (SF) and/or Machine Learning (ML). The openings deal with pedestrian/cyclist safety for highly automated vehicles and are part of larger collaborative efforts at Dutch and EU level.
The intended research addresses problems within the spectrum of object detection, pose estimation, motion modeling, tracking, and intent recognition from multi-sensor data (video, radar, lidar). Deep Learning is a promising research avenue but other ML methodologies might apply as well.
We are seeking applicants with an interest in performing cutting edge research in an active and exciting research area (cf. self-driving cars by the automotive/hi-tech industry). Prospective applicants should have a strong academic record with solid background in computer science or engineering. Extensive experience in CV, SF or ML is a requirement for the Post-Doc position; it is a strong plus for the Ph.D.-Student position. Good programming skills are expected, preferably in C/C++ and MATLAB/Python.
The appointments are full time (38 hrs/wk). The Post-Doc appointment will be for an initial period of 2 yrs. (renewable for a 3rd. year). The Ph.D. student appointment will be for a period of 4 yrs. Salaries are in accordance with the university regulations for academic personnel (Post-Doc salary EUR 2919-3831, Ph.D.-Student salary EUR 2083-2664).
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 2015/2016 in Engin. and Techn.).
Application Instructions: Applications should be directed to Prof. Dariu Gavrila (see www.gavrila.net for contact data) and include a motivation letter, a CV, BS/MS transcripts including grades, MS Thesis (plus Ph.D. Thesis for the PostDoc level), a list of projects and/or publications and the names of two references.