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Lead PI: Olaf Hellwich

Description of the project:

Visual understanding is a key component of biological and synthetic intelligent systems. As visual sensors (of any kind) provide high-dimensional data vectors with structural relationships between vector elements, such as multi-channel 2D images, the analysis of visual data unavoidably is a search problem in highly complex spaces. This is especially true if the visual input has a time component as in the visual system of an acting agent. Therefore, the goal of this project is it to develop a modularized and hierarchical temporal vision system for representation learning as a basis for a closed perception-action loop. The system is supposed to allow unsupervised learning of task relevant representations by leveraging the additional information contained in the time domain and compensating for the low information density in video streams.


  • Conducting experimental research in computer vision
  • Analysis of video data to generate algorithms for computer vision
  • Automated evaluation of behavior
  • Modeling of behavior using representation and reinforcement learning 
  • Interaction within the SCIoI cluster of excellence
  • Compilation of the results for presentations, project reports, and publications


Applicants must hold a Diploma/Master’s degree in computer science, engineering, physics or mathematics. The ideal candidate has a background in computer vision with strong expertise in machine learning. The successful applicant should have

  • excellent mathematical skills,
  • in depth programming skills (C/C++, Python, Matlab),
  • very good command of English, both written and spoken,
  • strong interest in visual perception and machine learning,
  • a keen interest in understanding intelligence,
  • the strong communicative skills required for interdisciplinary research,
  • a conscientious work approach, flexibility, good time management, and ability to work in a team.


Application deadline: 29 November 2019 23:59 CET