Research Fellow- adaptive Learning Strategies in

vor 1 Woche


Berlin, Deutschland Science of Intelligence Vollzeit

**Cluster of Excellence Science of Intelligence**

**Reference number**:SCIoI-C5-50B**

The Humboldt-Universität zu Berlin (Faculty of Mathematics and Natural Sciences

**Job description/**doctoral project**: Adaptive learning strategies in robots for social
interactions

In social interactions between human learners and teachers, it is of central importance that the teacher develops a model of the learner that refers to knowledge, motivation, and emotion of the learner - such a model allows to match tasks and the provision of learning support to the learner. In turn, the learner needs to develop a model of the teacher that informs about the knowledge, teaching style and individual differences such as trustworthiness or patience of the teacher. The development of such a mutual
understanding is a core component of successful social interactions in learning contexts.
However, it is empirically not well researched in instructional psychology how learners and teachers build such mutual models of each other-and thus, how tasks, materials, learning content and social interactions can be adapted to the characteristics of the learner or the teacher. In robotics, on the other hand, the characteristics of synthetic learners and teachers can be changed, therefore the question is more, how the characteristics of learners and teachers can optimally be adapted to the requirements of the task, the current context, and specific characteristics of their interaction partners.

In this project, we aim to investigate whether and how principles of adaptive social
interaction among humans can be transferred to social interactions between humans and
artificial agents (HRI), and between artificial agents (RRI).

The doctoral project focuses on adaptive learning strategies in Human-Robot Interaction
and in Robot-Robot Interaction. One aim is to develop adaptive learning strategies in
robots that can guide the learning process depending on the context, task difficulty, current knowledge of the robot and specific characteristics of the teacher. We will investigate learning and exploration strategies such as intrinsic motivation systems and evaluate them based on learning success and insights from learning theories in humans.

Responsibilities include scientific research within the project and academic services in the Cluster. The postdoc positions requires participation in research colloquia, lecture series and workshops, as well as an active engagement in the Cluster's research activities and close collaboration with a second theoretical postdoc hired in the same project.

**Requirements**:

- diploma or master’s degree in computer science, robotics, machine

learning or computational neuroscience
- in-depth programming skills in Python
- very good command of English (spoken and written)
- strong interest in HRI, machine learning and 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

Please click here for the legally binding German version.



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