PhD Studentship: Embodying intelligence in robotic materials

Emerging Scholars Council; Birmingham (AL); On-site; USD 27,000 - 33,000; Full time

Benefits offered by this job

UKRI stipend

Mentorship opportunities

Conference exposure

International collaborations

Job summary

Binysh Lab at the University of Birmingham is offering a PhD focused on robotic materials and active mechanical networks, combining experiments and modelling. The project explores physical reservoir computing, contrastive learning, and decision-making paradigms in embodied AI.

You will develop decentralized learning methods, model nonlinear dynamics, and conduct tabletop experiments while coordinating with supervisors and international partners.

Qualifications

• MSc/MPhys/MEng in physics, mechanical engineering, computer science, robotics, applied mathematics or equivalent.
• Interest in combining table-top experiments with simulations and theory.

Responsibilities

• Lead research into robotic materials that adapt dynamics after deployment.
• Develop decentralized learning techniques for networks of active mechanical units.
• Perform table-top robotic experiments and mentor master's students.
• Present findings at conferences and collaborate with international partners.

Job description

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!

Note: For more details please 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.
• 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 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).

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 at [e-mail].

For relevant publications, details of research environment and our commitment to inclusivity, see https://binyshlab.com/positions/. Questions? Email Dr. Binysh at [e-mail].