Doctoral Project: Quantifying and Modelling Collective Behavior of Fish across Ecological Contexts

Part of research project: Learning of intelligent swarm behavior

Principal Investigators

Description of the doctoral project

The main objective of the project is to systematic quantification of collective behavior of fish in order to derive data-driven models of observed collective behavior across different ecological contexts. Here, two important ecological contexts shall be compared: collective exploration and collective predator response. The specific tasks involve the development of a parametric description of the behavioral repertoire of individual fish within a fish school from existing video tracking data, as well as a quantitative, comparative analysis of the experimental data across the different ecological scenarios using model inference and information theoretic approaches, both at the micro-scale (individual behavior) as well as the macro-scale (collective behavior). The project involves also the acquisition of additional experimental data for the collective behavior of Poeciliids in different ecological contexts in laboratory as well as in the field.

 

Project start date: October 1, 2019

Prerequisites

We search for applicants with a Diploma/Master’s degree in quantitative/computational biology, biophysics, bioinformatics or related natural sciences, with strong interest in applying advanced computational methods to behavioral biology and ecology. Applicants should have proven skills and background in the following topics:

  • Experience with writing scripts for modeling and data processing (Python, R)
  • Experience with video tracking of animals (ideally fish) or data-driven, individual-based models of collective behavior.

Further, previous experience in either of the following areas are strong additional assets:

  • experimental work on animal collective behavior
  • 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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