Doctoral Project: Modularized Visual Understanding for Perception-Action Loops

Principal Investigator

Description of the research 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

Application procedure

Applications should include: motivation letter, curriculum vitae, transcripts of records (for both BSc and MSc + doctoral degree if applicaple), copies of degree certificates (BSc, MSc), abstracts of Bachelor-, Master-thesis, e.g. doctoral thesis, list of publications and one selected manuscript (if applicable), two names of qualified persons who are willing to provide references, and any documents candidates feel may help us assess their competence.

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired.

Qualified individuals with disabilities will be favored. Applications are also expressly welcomed
from suitably qualified persons seeking to be entered as “gender diverse” in the public register.

The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities.

Please send copies only. Original documents will not be returned.


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

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