About this position
Join us!
Are you a curiosity-driven scientist with a background in physics, engineering, or a related discipline, who wants to lead work at the intersection of robotics and metamaterials? Join us!
This unique research opportunity revolves around the physics of distributed robotic systems, mechanical metamaterials, active matter, and embodied 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 ensure the work aligns with your goals and aspirations. For more details 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, 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?
- - 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.
- - You are motivated to combine table-top robotic experiments with simulations and theory, across your core expertise and beyond.
- - You should be proficient in spoken and written English (IELTS 6.0 with no less than 5.5 in any band 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 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 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 contact Dr. Binysh 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 .
Funding
GBP 21,805 per year
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Email the contact below with your CV and a short, specific message. See Emailing Professors.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.