Doctoral Project: Collective Adaptation and Learning in Fish Schools

Part of research project: Dynamical Collective Adaptation & Learning

Principal Investigators

Description of the research project

This project aims at understanding collective intelligence in fish schools and in artificial robotic swarms. The main focus of the project is the dynamical adaptation of collectives – How do individuals within the collective acquire and share knowledge in complex exploration scenarios? In particular, we will consider the collective behavior of fish in a complex, collective foraging scenario, where we will investigate the role of group size and group diversity. Here, we will specifically explore collective behavior with partial, potentially conflicting or even erroneous, prior knowledge of individual agents. Finally, we will also investigate how diversity in prior knowledge interacts with consistent among-individual differences in behavior (i.e. animal personalities) within the collective.

Description of the doctoral project

The collaborative project investigates mechanisms underlying collective exploration and problem solving of fish schools, in close collaboration with researchers working on swarm robotics.
The main objective is to understand the benefits of grouping for collective cognition in fish schools, in particular the specific role of group size and group diversity for collective dynamical adaptation and learning. A specific focus of the project is the role of partial, potentially conflicting prior information of individuals within the group. The project involves systematic experimental investigation of a complex foraging paradigm for fish. The experimental work involves training and testing of (collective) fish behavior in a laboratory setting with two different poeciliidae species: Trinidadian guppies, Poecilia reticulata, and Amazon mollies, Poecilia formosa.


Project start date: October 1, 2019 (an earlier starting date may be possible)


We search for applicants with a Diploma/Master’s degree in biology or related natural sciences, with strong interest in quantitative behavioral biology and interdisciplinary collaborations. Applicants should have proven skills and background in the following topics:

  • quantitative, behavioral experiments (ideally with fish/poeciliids)
  • experience with using/writing scripts for data processing and modeling (R, Python)
  • experience with video tracking
  • very good command of English, both written and spoken

Furthermore, previous experience in the following areas are strong additional assets:

  •  collective behavior of fish
  • experience in statistical analysis of experimental data (e.g. mixed model approaches, repeatability analyses, etc)
  • automated data acquisition and processing, including automated quantification/classification of animal behavior
  • advanced computational methods (e.g. model inference, machine learning)


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