PhD studentship: Physical intelligence in robotic materials
University of Birmingham
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 wi...
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 wi...
Opportunity details
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...
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 ensure the work 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 ensure the work 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 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.
Open the official application information
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Source and verification
Check the current official call before applying
Recorded source check: August 17, 2026. A stored check date does not guarantee that every field is complete or still current. Compare the provider, eligibility, funding and deadline with the live official call before applying.
Benefits
Funding eligibility: UK Students. Funding amount: £21,805.
Eligibility summary
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. - 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 ensure the work 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.
Documents required
CV, references, cover letter
Language/test requirements
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