PhD Position in ML for Sensing and Shared Control in Autonomous Robot Systems Navigation

We are looking for creative and highly motivated researchers to join our team at the Spinal Cord Injury Artificial Intelligence - SCAI Lab at ETH Zurich. This opening is for a doctoral position in autonomous robot navigation and personalization for shared control. We will investigate fast and efficient embedded scene understanding for navigation with highly dynamic mobility devices in collaboration with industrial partners. 
Our team of clinical and research scientists is dedicated to improving the long-term prognosis of Spinal Cord Injury (SCI) through new health care systems using physiological and clinical data analysis. We are excited to embark on a new project that will bring us closer to delivering freedom of motion through novel personal mobility solutions.
Apart from actively shaping our group's research, the positions include international collaborations with academia and industry, and mentoring MSc students.

Dr Diego Paez-Granados and Prof. Robert Riener (SMS Lab) will supervise you. 

The position is funded for up to 4 years. Ideal starting date: May 2023 (or shortly after).

At SCAI Lab, you will have the unique opportunity to work at the intersection of clinical settings and lab environments, gaining exposure to intelligent health management systems. You will work alongside a team of highly motivated young professionals who are passionate about developing new methods for robotics rehabilitation, decision-making support in clinical practice, and clinical studies using advanced ML for monitoring and modelling diseases.

As a team member, you will experience a range of exciting challenges, including multimodal data analysis, developing innovative technologies, and collaborating with leading experts in the field. You will be based between ETH Zurich and one of Switzerland's largest and most prestigious health providers: the Swiss Paraplegic Center (SPZ) in Nottwil.

In this project our lab is focusing on advancing scene understanding through onboard sensing, enabling real-time evaluation of the environment for full autonomy and shared control with dynamic path planning. Our goal is to leverage advanced real-time sensor fusion and a novel personal mobility device with holonomic capabilities to achieve highly reactive shared control at different levels of autonomy.

The goal of this project is to leverage advanced real-time sensor fusion and a novel personal mobility device with holonomic capabilities to achieve highly reactive shared control at different levels of autonomy.

You will be responsible for implementing clean stable software, researching novel methods of shared control navigation using highly efficient sensor fusion and delivering novel algorithms in motion control and scene understanding for navigation.

You have outstanding experience in mechatronics systems, and control with a Master's degree from a university in Computer Science, Robotics, Mechanical Engineering, Engineering, or related fields.

• Highly motivated, self-driven, and shows excellent performance in your studies.
• Excellent communication and interpersonal skills are also required.
• You have first-rate oral and written English skills. Proficiency in German is a plus but not a requirement.
• Experience with good coding practices, code and review processes in C/C++/C# or Python.
• Strong practical skills in integrating embedded motor control systems.
• (Preferred) A proven track record in robot control or ML in computer vision.

This is a unique opportunity to be at the forefront of cutting-edge research in AI embodied for robots, working with state-of-the-art technology and collaborating with some of the brightest minds in the field.

We offer a full-time research position (100%) with a competitive salary in accordance with ETH standards.

The work location should be mostly at ETH Zurich, with flexibility for remote work possible.

If you are a highly motivated and creative individual with a passion for innovation, we want to hear from you. Join us and help shape the future of shared control navigation and personal mobility devices.