Research Assistant (Doctoral Position), salary grade E13 TV-L

(under the reserve that grants are funded. Part-time employment may be possible)

Faculty IV – Institute of Electrical Engineering and Computer Science / Cluster of Excellence: Science of Intelligence

Reference number: SCIOI-274/20 (starting at 01/10/20 / until 30/09/23 / closing date for applications 30/06/20)

The Technische Universität Berlin invites applications for a doctoral position for the Cluster of Excellence “Science of Intelligence”.

What are the principles of intelligence, shared by all forms of intelligence, no matter whether artificial or biological, whether robot, computer program, human, or animal? And how can we apply these principles to create intelligent technology? Answering these questions – in an ethically responsible way – is the central scientific objective of the new Cluster of Excellence Science of Intelligence (, where researchers from a large number of analytic and synthetic disciplines – artificial intelligence, machine learning, control, robotics, computer vision, behavioral biology, psychology, educational science, neuroscience, and philosophy – join forces to create a multi-disciplinary research program across universities and research institutes in Berlin. Interdisciplinary research projects have been defined (, which combine analytic and synthetic research and which address key aspects of individual, social, and collective intelligence.

Working field

 Efficient Robot Learning and Exploration in Real-World Tasks

Description of the doctoral project: To maximize progress towards solving a task, a robot must choose and execute its actions wisely. Many factors influence which choice of an action is the most appropriate one in a given situation. The factors include the physical abilities of the robot, the computational requirements of the task, the physical structure of the environment, and past experiences in similar situations. Given the complexity of these factors, the learning problem is too complex to be solved for a robotic platform operating in the real world, where the acquisition of data is costly and possibly dangerous to the robot. In this project, we will develop novel deep and hybrid learning methods to enable robust and sample-efficient learning in these settings. To achieve this goal, we will propose and test learning methods that incorporate various forms of priors and biases, ranging from past experiences to dynamic simulations, and from human strategies identified in cognitive psychology to robotic and algorithmic priors. These algorithms will be validated on real-world robotic systems.


  • Successfully completed university degree (Master, Diplom or equivalent) in computer science or similar field
  • Research experience in robotics and (deep) machine learning required
  • Research experience in computer vision, and/or control desired
  • Basic knowledge in cognitive science and psychology is a plus
  • Interest in interdisciplinary research in the context of the Cluster of Excellence “Science of Intelligence”
  • Excellent software engineering and programming skills in C++ and Python
  • Excellent English writing and communication skills; the willingness to learn German is expected

Application procedure

Please upload your application via the portal in order to receive full consideration. Reference number: SCIOI-274/20 (starting at 01/10/20 / until 30/09/23 / closing date for applications 30/06/20)

Applications should include: motivation letter, curriculum vitae, transcripts of records (for both BSc and MSc), copies of degree certificates (BSc, MSc, PhD if applicable), abstracts of Bachelor-, Master- and (if applicable) PhD-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.

By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guarantee for the protection of your personal data when submitted as unprotected file.

Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at the TU staff department homepage: or quick access 214041.

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

Technische Universität Berlin – Der Präsident – Fakultät IV, Exzellenzcluster Science of Intelligence, Prof. Dr. Oliver Brock, Sekr. SCIOI, Marchstraße 23, 10587 Berlin