vendredi 22 janvier 2010

Postdoctoral positions in collective animal behaviour: experiments and computational methods

Two postdoctoral positions are available in the Collective Behaviour Group, at the Centre for Statistical Mechanics and Complexity - CNR Rome. The positions are funded by the IIT project ART-SWARM, focusing on the experimental and theoretical study of collective behaviour in bird flocks and insect swarms, and its potential applications to artificial systems.

The candidates will work under the supervision of Irene Giardina and Andrea Cavagna. Information on our collective behaviour research can be found at:

A more thorough description of the candidates profiles and of the project's aims can be found below.

Each position is for 1+1 year, starting as early as March 2010 and not later than October 2010. Salary will be in line with Marie Curie (EC) standards. Applicants should send CV, publications list, research interests, and at least two recommendation letters to:

Dr Irene Giardina (subject: postdoc ART-SWARM)

In order to receive full consideration applications should arrive within February 28, 2010.


Both candidates must have a strong interest in collective phenomena in the physical and/or biological sciences. Although the two postdocs will work in a highly integrated fashion, the two positions have different and complementary scientific profiles:


The postdoc will be part of an experimental team of 3 people; ideally (but not necessarily) he/she will be the team leader. Work will include: setting up a new experimental apparatus for 3d swarm reconstruction; calibration and testing; preparatory field observations; field data-taking; data analysis.

* Prerequisites: background in either experimental physics, or experimental biology, or engineering; good computer skills.

* Bonuses (by no means necessary):
- field work
- Unix/Linux knowledge
- camera/video equipment
- practical stereoscopy
- electronics
- mechanics lab equipment



The postdoc will work on the computational tools needed to perform the 3d reconstruction, i.e. to transform the experimental digital images in a 3d data set. He/she will also work on dynamical tracking, in order to produce the full individual trajectories. Finally, the 3d data will be analyzed looking for new biological patterns.

* Prerequisites: background in either statistical physics or computer science; strong programming experience in C++; excellent Unix/Linux knowledge; basic script programming experience (Python/Pearl/...).

* Bonuses (by no means necessary):
- computer vision
- 3d reconstruction
- image processing
- optimization
- montecarlo methods
- numerical simulations
- html/php/sql
- openCV


From self-organized animal groups to distributed artificial swarms: exporting natural behavioral rules to mobile robotics

The study of self-organization and collective behavior encompasses fields as diverse as statistical physics, ethology, mathematical biology, control theory, and cooperative robotics. Three-dimensional animal aggregations, as bird flocks, fish schools and insect swarms, provide wonderful examples of emergent self-organization. The major issue, both for theoretical studies and for technological applications, is to understand how self-organization emerges within a system with distributed intelligence. Several multi-agent models of locking and swarming exist, which produce collective behavior starting from simple rules followed by the individuals. Yet, due to the lack of 3D large-scale data, these models are hardly tested against quantitative observations. Moreover, the rules of interaction among the agents are guessed on the basis of common sense, rather than being quantitatively modelled on empirical observations. This is a severe limit. In biological groups individual strategies are selected by evolution to achieve functioning and overall efficiency at collective level. Thus, empirically based information on these
strategies would not only lead to more appropriate models, but also help to design optimal control strategies in artificial systems.

This project has the following objectives:

1. Observe. We will perform quantitative field studies of bird flocks and insect swarms. Using innovative techniques in computer vision, we will reconstruct individual 3D positions and dynamical trajectories in cohesive aggregations of thousands of animals.

2. Understand. By analyzing the data, our aim is to unveil the laws of self-organization and collective behavior in 3D animal aggregations. Spatial and dynamical correlations among the individuals will provide a full characterization of the rules of interaction in the animal groups considered.

3. Discover. Dealing with several species and phyla, endowed with specific individual abilities and facing different collective tasks, we will investigate the crucial link between sensory/cognitive functions and behavioral strategies, and determine how individual cognition regulates group coordination.

4. Model. We will exploit the insight gained from empirical data as an input to develop new 3D models of animal collective behavior. The tools will be multi-agent theory and mathematical biology. Models output will be quantitatively tested against 3D data.

5. Export. We will design new schemes of distributed control quantitatively modelled on 3D animal behavior. Target applications will be cooperative mobile robotics for environmental monitoring, and nanorobotics for medical applications.


Irene Giardina
SMC-INFM, Department of Physics, University of Rome La Sapienza, P.le A. Moro 2, 00185 Rome, Italy
ISC-CNR, Via dei Taurini 19, 00185 Roma, Italy
tel: 0039-06-49937460 (ISC) fax: 0039-06-49937440 (ISC) fax: 0039-06-4957697 (Dept.)