Doctoral researcher, Project: 'Perception, Categorization and Synthetization of social responsiveness in human-human and human-robot interaction during learning situations'

(salary grade E13-TV-L, under the reserve that funds are granted, starting not later than from 1.10.2021 / for 3 years / closing date for applications 20.11.2020, Ref SCIoI-C3-31b)

The Technische Universität Berlin invites applications for a PhD 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, behavioural 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

Social responsiveness and its effects on learning in human-human and human-robot interaction

This interdisciplinary research projects combines research from educational psychology and computer vision to examine principles of social responsive teaching behaviors in social learning situations. Perceiving and appropriately reacting to social cues facilitate effective knowledge transfer between interaction participants, whether they be humans or humans and an artificial agent such as a robot. The main goal of this project therefore is to develop synthetic systems (robotic teaching assistants) with high-level perceptual capabilities in social learning situations and, in the course of that, synthetic systems that are able to simulate social responsive behaviors.



Doctoral project “Perception, Categorization and Synthetization of social responsiveness in human-human and human-robot interaction during learning situations”

The project focuses on the identification of teaching behaviors that can be labeled as ‘social responsive’ in learning situations. We aim to automatically understand relations between social responsive teaching behaviors and student engagement, emotion, and cognitive performance in Human-Human and Human-Robot interaction. One aim is to sensitively categorize behaviors that define social responsive teaching behaviors, to synthesize such behaviors and to apply synthesized behaviors in learning situations using robotic teaching assistants.



Applicants must hold a Diploma/Master’s degree in Computer Science or related sciences and should have proven skills/background in following topics:

  • profound expertise in computer vision and machine learning
  • expertise in robotics and visualization
  • excellent mathematical skills,
  • in depth programming skills (C/C++, Python, Matlab),
  • very good command of English, both written and spoken,
  • 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 procedure

Candidates should upload their application preferably via the portal in order to receive full consideration.

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