About the Project
PhD studentship: Embodying intelligence in robotic materials
Join us!
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!
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 nonlinear dynamics of these networks using theory and numerical tools e.g. finite-element modelling or discrete mechanical modelling.
- Explore fundamental physical questions on the link between network structure and functionality.
- 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?
- 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 open and 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).
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 and main supervisor (Jack Binysh). We aim to have you start in either Autumn 2026 or 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.
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, with second supervisor Dr. Mingchao Liu (Mechanical Engineering). The School’s £65m flagship building, based at the University’s Edgbaston campus, is a world-class environment for research across soft robotics, metamaterials, and sustainable robotics, featuring state-of-the-art resources as part of the £34.6M RESCu-M2 Hub, a £13.4M Research Hub on Collaborative AI for Manufacturing Sustainability, the Birmingham Institute for Robotics, and the 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 contact Dr. Binysh directly, 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 reach out to Dr. Binysh directly to discuss funding opportunities.
References
Interested candidates are invited to read the below references, which serve as a good guide to future work:
- J. 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
- S. 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.
- J. 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
- J. 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:
- O. Dauchot, From Active to Odd to Smart Matter, Phys. Rev. E 114, 011001 (2026). Preprint: https://arxiv.org/abs/2607.06051
- M. 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
- L. 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
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