Doctoral researcher, Project: 'Social responsiveness in human-human and human-robot interaction during learning situations in heterogeneous groups: Providing adaptive learning support through responsive behaviors'

(salary grade E13 TV-L, under the reserve that funds are granted, starting no later than from 1.10.2021 / for 3 years / closing date for applications 20.11.2020, Ref SCIoI-C3-31a)

The Universität Potsdam 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 learning in heterogeneous groups: Effects on 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 “Social responsiveness in human-human and human-robot interaction during learning situations in heterogeneous groups: Providing adaptive learning support through responsive behaviors”

The project focuses on the identification of teaching behaviors that can be labeled as ‘social responsive’ in learning situations. We aim to examine relations between social responsive teaching behaviors and student engagement, emotion, and cognitive performance in Human-Human and Human-Robot interaction. One aim is to better understand behaviors that define social responsive teaching behaviors, to synthetize such behaviors and to examine their relations to learning outcomes. To reach this goal, we apply classroom video observation (CLASS system) and quasi-experimental research to study social interaction in learning situations between humans as well as between humans and robotic teaching assistants.




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

  • Expertise in planning and realizing experimental studies in the field of learning and instruction
  • Expertise in classroom video observation
  • Excellent skills in statistical software (e.g. R, Mplus, SPSS)
  • Skills in video rating software (e.g., Videograph) is an advantage
  • Adequate knowledge of the English language is required, both written and spoken – excellent communication skills in English language
  • A conscientious work approach, flexibility, good time management, and ability to work in an interdisciplinary 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.