Research Associate in Swarm Robot Localisation, Mapping and Inspection

Overview

We are seeking an experienced researcher to develop decentralised swarm mapping and localisation algorithms for underground pipe operations as part of an international research programme funded by the Engineering and Physical Sciences Research Council (EPSRC). The programme is developing machine intelligence for teams of autonomous robots working in buried water infrastructure.

You will work on the design of methods that enable robot swarms to explore pipe networks, share information, support anomaly detection, and coordinate reporting and dispatch decisions in environments with limited communication, sensing and power. The role also includes validating these methods through simulations and physical robot experiments. The development of uncertainty aware methods, such as Gaussian processes with learning capabilities is essential, including data association and other Bayesian methods.

You will join a collaborative research environment at the University of Sheffield, working across the School of Electrical and Electronic Engineering, Centre for Machine Intelligence and Sheffield Robotics with international partners including TU Darmstadt, MIT and Boston University. There is an opportunity for extensive international travel to work closely with our overseas partners and to present research to peers at key international conferences.

You will help translate ambitious ideas into robust algorithms and convincing experimental demonstrations. You will also contribute to the strategic direction of this research area, support publications and project coordination, and help develop future funding applications and collaboration opportunities.

We are looking for someone with a PhD in robotics, computer science, control, AI or a closely related field, and a strong background in swarm robotics, multi‑robot systems or distributed autonomous systems. You should be able to work with a high degree of independence, contribute to collaborative leadership, and develop innovative solutions to technically challenging problems.

Main duties and responsibilities

• Develop novel decentralised swarm algorithms for mapping in underground pipe operations, with particular focus on exploration, collective decision‑making, information sharing, reporting and robot navigation.
• Develop sensor data fusion algorithms that are an essential element of the joint robot mapping tasks in the presence of data origin uncertainties. Knowledge of Bayesian approaches such as Gaussian process regression, particle filters, Bayesian networks and graph‑based approaches. Probabilistic‑based uncertainty quantification is also essential.
• Support the design, implementation and evaluation of bio‑inspired, optimisation‑based and machine‑learning‑enabled methods that enable robot teams to operate under constraints on communication, sensing, computation and power.
• Develop and maintain high‑quality research software for simulation, computer vision‑based algorithms (with optical videos, Lidar and other data), testing and deployment on robotic platforms, using reproducible and well‑documented development practices.
• Support the development of centralised and decentralised algorithms for the swarm of robots performing underground inspections.
• Support the planning and delivery of simulation studies and laboratory experiments to validate swarm algorithms, including coordinating their integration with related mapping, sensing and robotic platform activities across the wider programme.
• Analyse and interpret simulation and experimental data, using the findings to direct method refinement and inform project priorities, research decisions and long‑term development.
• Determine research objectives and develop, implement and adapt a programme of research in line with project aims, milestones and emerging opportunities.
• Contribute to the strategic direction and day‑to‑day coordination of this area of research, including planning work over several months ahead to meet project deadlines, publication targets and collaboration milestones.
• Work closely with academic staff, research colleagues and international partners, including international visits to support delivery of shared…