PhD Student Position: Efficient Deep Learning for Mapping and Localization of Intelligent Vehicles

TU Delft
Posted 2 months ago

The Intelligent Vehicles group at TU Delft is seeking a PhD candidate with an interest in performing cutting edge research in the area of self-driving vehicles, in collaboration with TomTom, a global leader in mapping and navigation products.

Currently, highly automated vehicles commonly rely on detailed 3D maps created with SLAM algorithms and LIDAR data for accurate self-localization. However, these representations do not scale, are sensitive to changes in the environment, are sensor specific, and also computationally intensive. TomTom’s research product RoadDNA takes an alternative approaches to represent the road environment more efficiently. However, it uses a hand-engineered representation, which is mainly target at highway environments sensed with LIDAR at a fixed compression rate.

This PhD student will instead develop optimized representations for mapping and localization in complex urban environments by learning robust semantic feature representations through end-to-end weakly-supervised deep-learning. The novel methods can additionally support rough priors provided by GPS, structural priors from aerial imagery and existing map data, or even temporal context. A learned representation can thus focus on features which matter most in the local area, and henceforth reduce its size, and increase localization efficiency. Higher-level features are additionally more robust against environmental changes, and could be transferred between multi-modal sensor setups or multiple viewpoints.

Applicants should have a strong academic record with a solid background in computation, sensor processing (e.g. computer vision), machine learning and AI. Good programming skills are expected, preferably in C++ and MATLAB/Python. Knowledge of deep-learning frameworks (TensorFlow/Torch/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. Applicant(s) are 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 Ph.D.-Student appointment is full time (38 hours a week) and will be for a period of four years (the initial employment is 18 months and after a positive evaluation, the appointment will be extended further with 30 months) and should lead to a dissertation. Salary is in accordance with the university regulations for academic personnel. The PhD-Student salary will range from €2174 (first year) up to a maximum of €2779 (last year). The figures is before tax per month based on a full-time appointment. Secondary benefits amount to an additional 16.3% of yearly salary.

Research will be performed in collaboration with TomTom R&D, in Amsterdam, where the candidate is expected to work on average about 0,2 FTE.

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 Engineering and Technology).

For more information about this position, please contact Dr. J. F. P. Kooij (e-mail: To apply, please submit

  • a letter of motivation explaining why you are the right candidate,
  • a detailed CV,
  • a complete record of Bachelor and Master courses (including grades),
  • a link to your Master’s Thesis (at least as draft)
  • any publications, and a list of projects you have worked on with brief descriptions of your contributions (max 2 pages), and
  • the names and contact addresses of two references.

All these items should be combined in one PDF document. Applications should be submitted as soon as possible by email to When applying for this position, please refer to vacancy number 3ME18-44 in the subject of the email. DO NOT USE THE ONLINE FORM BELOW.

Application deadline is September 1st, 2018.

Job Features

Application deadline01.09.2018

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