PhD in Design and Simulation of Distributed Autonomous Control of a Robot Swarm for Inspection Applications

Over the past two decades, the field of swarm and multi-robot systems has made significant progress in devel oping ground, aerial, and aquatic robot collectives that exhibit diverse collective behaviours. A key open challenge, however, is the deployment of large groups of resource-constrained robots that can sense, process data, and make decisions locally while operating in complex environments. This PhD project focuses on the design, implementa tion, and experimental evaluation of a new generation of miniaturized mobile robots, equipped with sensing, onboard computation, and wireless communication, that can operate in coordinated groups for sensing and inspection tasks.

What are you going to do?

You will develop multi-level simulation models that capture robot mobility, sensing, communication, and collec tive decision-making under uncertainty. These models will enable principled analysis of swarm behaviour and will support the design of decentralized algorithms for coordination, task allocation, and adaptive coverage at large scales. In parallel, you will investigate data-driven methods to support offline analysis and inform high-level decision making within the swarm. The simulation tools and algorithms you develop will be validated both in software and through experiments using our physical robot platforms. The position offers the opportunity to work on fundamen tal questions at the intersection of robotics, distributed AI, and complex systems. You will contribute to a broader long-term research agenda on scalable autonomous robot collectives for inspection and monitoring applications.

• A Master’s degree in Robotics, Computer Science, AI, Applied Mathematics, Systems and Control, or related fields. Candidates with a Bachelor’s degree may also be considered for admission if they can demonstrate evidence of extensive research experience.
• Fluency in spoken and written English. An official test certificate such as TOEFL or IELTS is a plus. If you do not have a certificate, English fluency will be evaluated during the initial admission interview.
• Strong programming experience in Python and/or C++. Experience with robot simulators (Webots, Gazebo, IsaacSim, etc.), multi-agent systems, control theory, or numerical modelling is highly desirable. Prior experi ence with AI/ML methods for data processing is a strong plus.

• 232 vacation hours per year, based on a 38-hour workweek (1.0 FTE). You can also work more or fewer hours in exchange for more or fewer free hours. For example, with a 40-hour workweek, you save 96 extra free hours, and with a 36-hour workweek, you lose 96 hours.
• End-of-year bonus of 8.3% and 8% holiday allowance.
• Extensive opportunities for personal and professional development.

At the University of Groningen (UG), researchers from all fields of academia and technology are working on academic challenges and societal questions. Lecturers prepare their students for meaningful careers within or outside the academic world. Interdisciplinary research and teaching, sharing of knowledge, collaboration with businesses, government institutions, and societal organizations are aspects that are of the utmost importance to this European top university. The UG aims to be an open academic community with an inclusive and safe working climate that invites you to add your value.

This PhD project is planned as a four-year PhD programme at the University of Groningen, in Groningen, The Netherlands and will be funded through a recent NWO Vidi grant. The University of Groningen ranks among the top universities worldwide and has a very strong international reputation in automation and control.

Supervisor: Dr Bahar Haghighat, https://www.rug.nl/staff/bahar.haghighat/

About the PhD supervisor

Dr Bahar Haghighat is currently a Tenure Track Assistant Professor of Robotics and Automation at the Engineering and Technology institute Groningen (ENTEG), in Groningen, the Netherlands. Bahar’s work investigates building miniaturized robotic swarms and algorithmic frameworks that enable sensing, surveying, and inspection applications. Her research involves mechatronics, embedded systems, and AI/ML models. Her work aims to produce novel surface, aquatic, and aerial miniaturized robot swarms and small-scale intelligent devices that can benefit several commercially promising applications in domains such as inspection of complex structures, environmental monitoring, and search-and-rescue robot swarms. Her research group is embedded within the Discrete Technology & Production Automation (DTPA) group at ENTEG. 

Dr Bahar Haghighat obtained her PhD in Robotics, Control, and Intelligent Systems in 2018 under the supervision of Prof. Alcherio Martinoli from the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, and her Master’s and Bachelor’s degrees respectively in Electrical Engineering/Digital Electronics and Electrical Engineering/Physics (double major) from Sharif University of Technology (SUT) in Tehran, Iran. She has been a postdoctoral research fellow at Harvard University and a postdoctoral research associate at Princeton University under the supervision of Prof. Radhika Nagpal. Her PhD research focused on mechatronic design and development of an aquatic swarm of miniaturized resource-constrained robotic modules capable of performing self assembly. Her postdoctoral research focused on mechatronic design, algorithmic development, and modeling of a swarm of sensing robots for infrastructure-related applications. Bahar Haghighat has been selected as an EECS Rising Star in 2021 (MIT) and in 2019 (UIUC). She is the recipient of EPFL’s PhD research award of Gilbert Hausmann for the best PhD thesis in the fields of mechanical engineering, electricity, and physics (2019), EPFL distinction of excellence for a PhD thesis in Robotics, Control, and Intelligent Systems (2018), two Swiss National Science Foundation (SNSF) post-doctoral fellowship awards (2017 and 2019), and the third place in EPFL’s My Thesis in 180 Seconds competition (2017).

Do you have any questions or need more information?

Questions about the content of the job?
Bahar Haghighat (Assistant Professor): bahar.haghighat@rug.nl

Questions about your application process?
Friso Salverda (Human Resources Adviser): f.d.salverda@rug.nl