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Postdoctoral Associate Position in Simulations of Complex Systems

Opportunity for Cutting-edge Research in Complex Systems Simulations Applied to Urban Analytics

The Singapore University of Technology and Design (SUTD) seeks to fill a post-doctoral position in the area of complex systems simulations in the NRF-funded “New Urban Kampong” (NUK) Program. The NUK program aims at creating a cross-disciplinary platform through a public-private collaboration (HDB-SUTD-MIT-EDF) to rethink the future of housing in Singapore. Specifically, the NUK program consists of 4 integrated projects, and the open position is part of Project #4 on Urban Analytics in close collaboration with our industrial partner EDF. The objective of this project is to develop an urban analytics tool that incorporates the concept of Quality of Life (QoL) developed in Project #2. The selected candidate will have a unique opportunity to carry out a truly interdisciplinary work with an international team of outstanding researchers at SUTD. The primary focus will be to develop a population model using agent based modeling (ABM) approaches, with the final aim to integrate it into a large-scale simulation platform originally developed by EDF.

Research Task “Project #4”

Supervisors: Prof. Roland Bouffanais and Prof. Bige Tunçer (SUTD)

Collaborator: Mr. Benjamin Mousseau & Mr. Maxime Cassat (EDF)

Position Qualifications
Post-doctoral PhD holder with a background in discrete numerical simulations (agent based models, ABMs, and otherwise) and complex systems.  This position will investigate topics related to virtual population simulations. Prior work on complex systems theory, or ABMs, and some experience in the application field of urban environments would be a plus.

Duration: 1 year (with possibility of extension up to 3 years in total)


  • Successful candidates should have received a Ph.D. degree in Physics, Computational Science or a related field.
  • Research expertise in complexity science and discrete numerical simulations will be considered a plus.
  • Solid working knowledge in Python coding.
  • Ability to work both independently and in a team environment, and to take ownership of, and autonomously carry forward, major aspects of a research project.
  • Ability of approaching research problems with a system view, of working in a multidisciplinary team environment, problem solving skills and high creativity.
  • Candidates must present strong publication record, as well as excellent verbal and written communication skills.

How to Apply

Interested persons must send their updated curriculum vitae along with a brief statement of research to the following addresses: and

Please state “NUK Recruitment – P4 Urban Analytics” in the email subject. Positions will be available until filled; only short-listed candidates will be notified.

From schools of fish, to swarms of insects, to flocks of birds, many animals live and move in groups. They have no leader, no central coordinator, and yet manage to perform awe-inspiring coordinated displays of collective motion. These swarming behaviors are archetypal examples of how local coordination between nearby animals translates into an emerging global behavior. But how localized should this local coordination be? Is more interaction always better? Not all animal taxon swarms, and observations of flocks of starlings show that they limit their interaction to their six-to-seven nearest neighbors.

New simulations of predators attacking a swarm help explain these observations. The simulations show that the group has a higher chance of survival when members limit the amount of individuals they interact with during their collective motion. This work reveals the clear parallel between collective evasive maneuvers and the spread of information in social networks.

If one thinks of the predator's presence as a "signal" that propagates through a network, it is expected that the earlier an individual receives this signal, the better its chances are of avoiding the predator. Using classical models of behavioral spread through complex networks, researchers from the Singapore University of Technology and Design (SUTD) observed that the propagation speed is radically increased when limiting the average number of connections allowed. Thus, the insights gathered from the behavior of swarming animals can be applied to many problems in engineering and social sciences: from increasing the flexibility of the power grid and designing responsive swarms of robots, to improving crowd mobility and optimizing information spreading on social networks.

For all the benefits that coordination and collective behavior yields to the members of a group, it seems that when it comes to social interaction there can be too much of a good thing.

Principal investigator, SUTD Assistant Professor Roland Bouffanais said: "For a long time, it was assumed that the performance of a group improves by making it more connected. This research shows the unexpected detrimental effects of having too many connections for both living and artificial systems."



Source:  AAAS EurekAlert! Science News

2016 IEEE RAS Summer School on Multi-Robot Systems
6-10 June 2016 @ National University of Singapore, Republic of Singapore
Hosted & co-organized by
Co-organized by
Financially supported by

Program here


The organizing committee will comprise members of NUS, A*STAR, and SUTD supported by the co-chairs of the IEEE RAS Technical Committee on Multi-Robot Systems:

Bryan Kian Hsiang Low
Assistant Professor > Dept. Computer Science > NUS
Ph.D. Electrical & Computer Engineering > Carnegie Mellon University
Research interests: multi-robot/agent systems, machine learning, planning
Somchaya Liemhetcharat
Senior Engineer > Uber Advanced Technologies Center
Ph.D. Robotics > Carnegie Mellon University
Research interests: multi-robot/agent systems
Roland Bouffanais
Assistant Professor > SUTD
Ph.D. Science > Swiss Federal Institute of Technology Lausanne
Research interests: multi-robot systems, theoretical & computational complexity science
Robert Fitch
Senior Research Fellow > ACFR > The University of Sydney
Ph.D. Computer Science > Dartmouth College
Research interests: autonomous field robotics
Lorenzo Sabattini
Assistant Professor > Dept. Sciences and Methods for Engineering > University of Modena and Reggio Emilia
Ph.D. Control Systems and Operational Research > University of Bologna
Research interests: multi-robot systems, decentralized estimation & control, mobile robotics
Antonio Franchi
Permanent Researcher (CR1) > LAAS-CNRS
Ph.D. Control, Systems Theory, and Robotics > "La Sapienza" University of Rome
Research interests: multi-robot systems, autonomous systems and robotics
Nora Ayanian
Assistant Professor > Dept. Computer Science > University of Southern California
Ph.D. Mechanical Engineering > University of Pennsylvania
Research interests: multi-robot systems, autonomous systems and robotics


David Hsu
Professor > Dept. Computer Science > NUS
Ph.D. Computer Science > Stanford University
Research interests: robotics, planning
Marcelo H. Ang, Jr.
Associate Professor > Dept. Mechanical Engineering > NUS
Ph.D. Electrical Engineering > University of Rochester
Research interests: robotics, mechatronics, and applications of intelligent systems methodologies

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