PhD position in Differentiable Simulation and Machine Learning for Musculoskeletal Robotics in Zürich

At the Soft Robotics Laboratory, we offer a fully-funded doctoral position for computational design optimization of electrohydraulic musculoskeletal robots. Our research group creates artificial muscles and attaches them to a rigid skeleton with ligaments and tendons to create adaptable, complex robotic systems. To achieve design optimization, we first need a physically validated simulation of robots driven by new electrostatic/electrohydraulic artificial muscles. We then apply this simulation in optimization settings for, e.g., both shape and control, in a multi-variable multi-objective co-optimization setting. We, therefore, consider implementing differentiable solvers for this application, starting from first principles, and later developing a deep learning surrogate for faster, potentially real-time, computation.

This doctoral position will focus mainly on the simulation and computational aspects, while most hardware components are fabricated and built by colleagues in the Soft Robotics Laboratory. Simulation to reality validation needs to be performed through experimental setups that the candidate should be able to design and use. A strong interest in working hands-on with robotic systems and validating simulations in the real world on robots is desirable.

As a PhD student, you will develop and publish on new software frameworks and their physical validation. You will regularly present your work at international robotics and machine learning conferences. Your responsibilities will also include supervising bachelor and master students in their thesis works, supporting the Soft Robotics Laboratory in teaching its graduate classes, and in preparing grant proposals.

You are interested in the optimization of robots for specific functional requirements, and are motivated to independently explore various fields of research for combining their knowledge to achieve this goal. You are curious about novel technologies, and learning about new muscle actuator types and understanding their inner workings. You persevere through challenges faced throughout the project, and can quickly adapt when experiments do not deliver the desired results. You are a diligent worker that is driven to publish new insights and lead the research community forward by communicating your findings to both a smaller community of researchers, as well as to a broader public audience.

You have background knowledge in numerical simulations, computational modeling, and shape optimization. Ideally, you have previously worked with robotics and (differentiable) simulations, with hands-on experience testing robots for their performance, and matching with simulated results. Machine learning knowledge, especially in the field of deep learning for scientific computing, can be beneficial. Proficient communication skills in English are required.

You should have a computer science or engineering background, with BSc and MSc degrees, in computer science, mechanical or electrical engineering, computational engineering, applied physics, or a related field. Your academic record is outstanding.

ETH Zurich is a family-friendly employer with excellent working conditions. You can look forward to an exciting working environment, cultural diversity, and attractive offers and benefits.