PhD Studentship: Embodying intelligence in robotic materials

This unique research opportunity revolves around mechanical metamaterials, robotics, active matter physics, and embodied artificial intelligence, combining table-top experiments and theoretical modelling. The project will focus on self-learning active mechanical networks, but will be tailored to align with the interests and expertise of the successful candidate - we will mutually agree on the direction of the research, ensuring it aligns with your goals and aspirations. Sounds good? Join us! Note: For more detai...

Opportunity details

This unique research opportunity revolves around mechanical metamaterials, robotics, active matter physics, and embodied artificial intelligence, combining table-top experiments and theoretical modelling. The project will focus on self-learning active mechanical networks, but will be tailored to align with the interests and expertise of the successful candidate - we will mutually agree on the direction of the research, ensuring it aligns with your goals and aspirations. Sounds good? Join us! Note: For more details please see https://binyshlab.com/positions/. What will you do? Conventional robotic bodies rely on computationally intensive centralized control and struggle when faced with unpredictable environments. Yet nature overflows with simple organisms–from starfish to bacteria–that traverse rough terrain with no brain at all. These organisms distribute actuation, feedback and computation across their soft bodies, blurring the boundary between material and machine. Our work hints that key platforms to capture such material intelligence are ‘robotic materials’–mechanical networks built from many sensors and actuators that locally communicate with one another to achieve collective functionality. These active networks could enable next-generation bioinspired robots that operate without central control, withstand massive damage and adapt to ever-changing environments. In this PhD, you will lead research into robotic materials that adapt their dynamics to an environment after deployment, leveraging recent advances in physical reservoir computing, contrastive learning, and biological decision-making paradigms. You will: Develop and apply decentralized learning techniques to networks of active mechanical units to sculpt their dynamics and functionality. Capture the nonlin...

Eligibility

The project will focus on self-learning active mechanical networks, but will be tailored to align with the interests and expertise of the successful candidate - we will mutually agree on the direction of the research, ensuring it aligns with your goals and aspirations. You hold an excellent MSc/MPhys/MEng degree (or equivalent) in physics, mechanical engineering, computer science, robotics, applied mathematics or an equivalent scientific/engineering field. How to Apply Applicants should upload information via Birmingham’s Mechanical Engineering PhD portal here: https://www.birmingham.ac.uk/study/postgraduate/subjects/mechanical-engineering-courses/mechanical-engineering-phd, specifying the title and main supervisor (Jack Binysh). In your application include: A cover letter in which you describe your motivation and qualifications for the position.

Requirements

The project will focus on self-learning active mechanical networks, but will be tailored to align with the interests and expertise of the successful candidate - we will mutually agree on the direction of the research, ensuring it aligns with your goals and aspirations. You hold an excellent MSc/MPhys/MEng degree (or equivalent) in physics, mechanical engineering, computer science, robotics, applied mathematics or an equivalent scientific/engineering field. How to Apply Applicants should upload information via Birmingham’s Mechanical Engineering PhD portal here: https://www.birmingham.ac.uk/study/postgraduate/subjects/mechanical-engineering-courses/mechanical-engineering-phd, specifying the title and main supervisor (Jack Binysh). In your application include: A cover letter in which you describe your motivation and qualifications for the position.

Funding and benefits

Funding eligibility: UK Students. Funding amount: £21,805.

Required documents

CV, references, cover letter

How to apply

Applicants should upload information via Birmingham’s Mechanical Engineering PhD portal here: https://www.birmingham.ac.uk/study/postgraduate/subjects/mechanical-engineering-courses/mechanical-engineering-phd, specifying the title and main supervisor (Jack Binysh). We aim to have you start in either Autumn 2026 or January 2027. In your application include: A cover letter in which you describe your motivation and qualifications for the position. A CV which includes the contact information of two references. A transcript of your degree grades. Funding is currently available to cover Home UK students, i.e. covering fees and providing a stipend at UKRI rates (current stipend: £21,805 p.a.) for 42 months. Strong international candidates are encouraged to reach out to Dr. Binysh directly to discuss funding opportunities at j.binysh@bham.ac.uk. For relevant publications, details of research environment and our commitment to inclusivity, see https://binyshlab.com/positions/. Questions? Email Dr. Binysh at j.binysh@bham.ac.uk.

Open the official application information

Verify the deadline and final conditions on the official provider page before submitting an application.