PhD Studentship: The Physics of Learning in Robotic Metamaterials

Join us!

Are you a curiosity-driven scientist with a background in physics, engineering, applied mathematics or a related discipline? Join us to lead work at intersection of metamaterials, soft matter physics, and robotics. Look at https://binyshlab.com for some of our work.

This position revolves around the physics of active mechanical metamaterials and distributed robotic systems, combining table-top experiments with theoretical modelling. The project will focus on self-learning active mechanical networks but will be tailored to your interests and expertise.

What will you do?

Nature overflows with simple organisms–from starfish to bacteria–that function without a brain. In contrast to a conventional robot, which is centrally controlled and rigid, these living materials are mechanically soft, and spread actuation, feedback and computation right across their bodies, blurring the boundary between material and machine. Living systems are unconventional robots indeed.

Our work hints that key platforms to capture such material intelligence are ‘robotic metamaterials’–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 metamaterials that can adapt their dynamics to an environment after deployment. You will:

• Develop and apply decentralized learning techniques to networks of active mechanical units to sculpt their dynamics and functionality and explore fundamental physical questions on the link between network structure and functionality.
• Capture the nonlinear dynamics of these networks using theory and numerical simulations.
• Design and perform table-top robotic experiments that implement your learning algorithms in unpredictable environments.

Who are you?

This PhD is ideal for a curiosity-driven scientist interested in (i) the intersection between materials and machines and (ii) What robotics can teach us about physics, and vice versa. You might be a physicist interested in robotics, a mechanical engineer interested in bio-inspired materials, or a computer scientist/mathematician interested in applied nonlinear dynamics and optimization. We seek an openness to combining table-top robotic experiments with simulations and theory, and an excellent MSc/MPhys/MEng degree (or equivalent).

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 of the PhD position, and main supervisor (Jack Binysh). We aim to have you start in Early 2027.

In your application please 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 should look at https://www.birmingham.ac.uk/study/international/fees/scholarships, and contact Dr. Binysh if they have a viable plan for international funding.

For relevant publications, details of research environment and our commitment to inclusivity, see https://binyshlab.com/positions/. Questions? Email Dr. Binysh in your own words at j.binysh@bham.ac.uk.

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