However, enabling seamless Human-Robot Teaming (HRT) with multiple robots presents fundamental challenges: humans and robots must maintain shared situational awareness, coordinate their actions under dynamic conditions, adapt to changing circumstances by dynamically allocating attention and control authority, and ultimately communicate and interact seamlessly at various levels of abstraction. Unlike single-robot systems, fleet operations amplify complexity, as operators face increased cognitive demands and the interaction paradigm must scale beyond one-to-one control towards one-to-many collaboration. Current approaches often fail to support the fluid, adaptive teaming that characterizes effective human-human collaboration.

This PhD project will address critical challenges in enabling seamless and effective HRT with robot fleets. We seek candidates who will propose and pursue novel research in one or more of the following interconnected directions:

• Supervisory interfaces and intelligent support: Develop multimodal interfaces (combining visualizations, gestures, natural language, and auditory channels) and intelligent assistant agents that support operators in supervising fleet operations. These systems will proactively filter information, highlight anomalies, diagnose problems, and recommend interventions to reduce cognitive workload and maintain situational awareness across multiple robots.
• Variable Autonomy: Develop frameworks for dynamically adjusting robot levels of autonomy based on task demands, environmental conditions, and human/robot capabilities and state.
• Shared Understanding between humans and robots: Develop computational shared mental models and shared situational awareness that enable the HRT to maintain common ground.

Your research direction will be shaped by the synergy between your interests and background, which you will refine into a detailed proposal during the first months of the PhD. We welcome candidates with backgrounds in Computer Science, Engineering, Robotics, Artificial Intelligence, Cognitive Science, or Human-Computer Interaction. Strong programming skills and experience with autonomous systems or human factors research are particularly valuable.

The PhD student will receive tuition fees at the home rate and a London stipend at QMUL stipend rates (currently in 2026/27 of £22,618 per year, to be confirmed for subsequent years) annually during the PhD period, which can span for 3 years. Non-home/non-UK students may apply, but if accepted, they will be responsible for the substantial difference in tuition fees, as no additional funding or fee waiver is available to cover this gap.

For more information about the project, please contact Manolis Chiou (m.chiou@qmul.ac.uk).

However, enabling seamless Human-Robot Teaming (HRT) with multiple robots presents fundamental challenges: humans and robots must maintain shared situational awareness, coordinate their actions under dynamic conditions, adapt to changing circumstances by dynamically allocating attention and control authority, and ultimately communicate and interact seamlessly at various levels of abstraction. Unlike single-robot systems, fleet operations amplify complexity, as operators face increased cognitive demands and the interaction paradigm must scale beyond one-to-one control towards one-to-many collaboration. Current approaches often fail to support the fluid, adaptive teaming that characterizes effective human-human collaboration.

This PhD project will address critical challenges in enabling seamless and effective HRT with robot fleets. We seek candidates who will propose and pursue novel research in one or more of the following interconnected directions:

• Supervisory interfaces and intelligent support: Develop multimodal interfaces (combining visualizations, gestures, natural language, and auditory channels) and intelligent assistant agents that support operators in supervising fleet operations. These systems will proactively filter information, highlight anomalies, diagnose problems, and recommend interventions to reduce cognitive workload and maintain situational awareness across multiple robots.
• Variable Autonomy: Develop frameworks for dynamically adjusting robot levels of autonomy based on task demands, environmental conditions, and human/robot capabilities and state.
• Shared Understanding between humans and robots: Develop computational shared mental models and shared situational awareness that enable the HRT to maintain common ground.

Your research direction will be shaped by the synergy between your interests and background, which you will refine into a detailed proposal during the first months of the PhD. We welcome candidates with backgrounds in Computer Science, Engineering, Robotics, Artificial Intelligence, Cognitive Science, or Human-Computer Interaction. Strong programming skills and experience with autonomous systems or human factors research are particularly valuable.

The PhD student will receive tuition fees at the home rate and a London stipend at QMUL stipend rates (currently in 2026/27 of £22,618 per year, to be confirmed for subsequent years) annually during the PhD period, which can span for 3 years. Non-home/non-UK students may apply, but if accepted, they will be responsible for the substantial difference in tuition fees, as no additional funding or fee waiver is available to cover this gap.

For more information about the project, please contact Manolis Chiou (m.chiou@qmul.ac.uk).