PhD position in Computer Vision

Updated: about 2 months ago
Job Type: Temporary
Deadline: 29 Aug 2022

The University of Amsterdam (UvA) and TomTom have started a new research lab called "ATLAS Lab" consisting of one Tenure Track Assistant Professor and 6 PhD students. The focus of the new lab is on developing advanced, highly accurate and safe maps for automated vehicles (HD maps), using Artificial Intelligence. These HD maps include detailed geometric and semantic representations of elements in a road networks, such as lane dividers, traffic signs, traffic lights, junctions, etc. The ATLAS Lab is a research collaboration between UvA and TomTom (location Amsterdam), and is part of ICAI, the national Innovation Centre for AI, based in the Amsterdam Science Park.

For our new lab, we are seeking a PhD candidate in the field of computer vision and deep learning. Topics include (3D) object detection and segmentation within a diverse set of learning paradigms (e.g. weakly/ semi/ self-supervised), and quantifying uncertainty. Sensor data is assumed to be multi-modal and may include RGB, RGB-D, LiDAR, and positional sensors. You will be working on fundamental aspects of deep learning models and algorithms.

What are you going to do

You are going to carry out computer vision and machine learning research as part of the Atlas lab (in total 6 PhD students of which 4 of them are already hired) at the University of Amsterdam. There will also be regular visits to and interactions with researchers at TomTom (Amsterdam). At the University of Amsterdam, you will be supervised by prof. Gevers.

Tasks and responsibilities:

  • Develop new machine learning methods within the context of computer vision;
  • Collaborate with other researchers within the lab and TomTom;
  • Complete and defend a PhD thesis within the official appointment duration of four years;
  • Regularly present intermediate research results at international conferences and workshops, and publish them in proceedings and journals;
  • Assist in relevant teaching activities.

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