PhD Studentship: The Physics of Learning in Robotic Metamaterials

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, who wants to lead work at the intersection of metamaterials, soft matter physics, and robotics? Join us! And look at binyshlab.com for some of our work.

This unique opportunity 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 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.

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 mechanically 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.

You will have opportunities to mentor master’s students, present your work at high-profile conferences, and interact with international partners across Europe and beyond. This role is a platform to advance the fields of metamaterials and robotics in an environment that values mentorship and collaboration.

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).

Our department is proud to foster a vibrant and inclusive postgraduate research community where researchers from all backgrounds feel valued and supported. We actively welcome applications from individuals of all backgrounds, particularly those from underrepresented groups in academia. Questions about your background, working patterns etc.? Contact Dr. Jack Binysh at j.binysh@bham.ac.uk.

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.
• A research proposal - Here, because this is an existing project, please feel free to use the job advert as a starting point, but do include any ideas you would be most excited to work on.

Embedding

You will be supervised by Dr. Jack Binysh, Assistant Professor in the Automation, Robotics and Control group in Mechanical Engineering at the University of Birmingham. Jack is an applied physicist working on robotic metamaterials for autonomous motion and smart robot bodies. His research combines mechatronic fabrication and prototyping with advances in continuum modelling, coarse-graining procedures and reduced-order simulations. This work has appeared in journals such as Nature, PNAS, Nature Photonics, Science Advances, Physical Review X & Physical Review Letters – to learn more, check out binyshlab.com. Birmingham itself provides a world class facility for soft robotics, metamaterials, and sustainable robotics, featuring a recently refurbished Makerspace with access to over 30 3D printers, metal and composite printing, machining, laser and waterjet cutting, PCB development, electronics facilities and technical support.

Information

Do you recognize yourself in this profile and would you like to know more? Please write to Dr. Binysh in your own words. Email: j.binysh@bham.ac.uk.

Funding Notes

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

References

Interested candidates are invited to read the below references, which serve as a good guide to future work:

1. Veenstra, C. Scheibner, M. Brandenbourger, J. Binysh, A. Souslov, V. Vitelli and C. Coulais, Adaptive Locomotion of Active Solids, Nature 639 935-941 (2025). Preprint: https://api.repository.cam.ac.uk/server/api/core/bitstreams/56179c32-32e9-4556-b5f2-59b721fb53bc/content
2. Al-Izzi, Y. Du, J. Veenstra, R. G. Morris, A. Souslov, A. Carlson, C. Coulais and J. Binysh, Non-reciprocal Buckling Makes Active Filaments Polyfunctional, PNAS 123 11 e2531723123 (2026). Preprint: https://arxiv.org/abs/2510.14725.
3. Binysh, J. Veenstra, V. Seinen, R. Naber, D. Robledo-Poisson, A. Hunt, W. van Saarlos, A. Souslov & C. Coulais, Wave Coarsening Drives Time Crystallization in Active Solids. Preprint: https://arxiv.org/abs/2508.20052
4. Binysh, G. Baardink, J. Veenstra, C. Coulais & A. Souslov, More is Less in Unpercolated Active Solids, PRX 16 021012 (2026). Preprint: https://arxiv.org/abs/2504.18362

Below are some additional, relevant references candidates may wish to read:

1. Dauchot, From Active to Odd to Smart Matter, Phys. Rev. E 114, 011001 (2026). Preprint: https://arxiv.org/abs/2607.06051
2. Stern and A. Murugan, Learning Without Neurons in Physical Systems, Ann. Rev. Cond. Matt. Phys. 14, 1 (2023). Preprint: https://arxiv.org/abs/2206.05831
3. van Laake and J. Overvelde, Bio-inspired autonomy in soft robots, Comm. Mater. 5, 1 (2024). Open Access PDF at https://www.nature.com/articles/s43246-024-00637-7