The University of Edinburgh is a world-class organisation. We look for the best in the field across all disciplines and provide a working environment where academics can develop their careers and passion for their chosen subject area. We offer the full range of academic roles and have a genuine focus on our student’s performance and wellbeing.

The Role

As research associate, you will conduct research within the Statistical Machine Learning and Motor Control (SLMC) group, part of the Institute of Perception, Action and Behaviour (IPAB). You will support the team in advancing the state of the art in whole-body, multi-contact robotic manipulation, developing and combining model-based methods (e.g. trajectory optimization, MPPI) with machine learning approaches (e.g. RL, diffusion policies) to scale and deploy multi-contact robot motions.

You will work with robotic manipulation hardware and sensing, including KUKA LBR robot arms and Kawada's Nextage NXA, and contribute to shared-autonomy methods for skilled teleoperation of robot manipulators. The role also involves collaboration and field deployment trips with project partners in Japan, alongside regular reporting on progress and results.

Desirable knowledge and experience:

• Experience mentoring undergraduate and master's students on research projects related to this research area
• Experience in robot learning techniques, such as reinforcement learning (RL) and/or flow matching and diffusion policies
• Experience in motion planning, model predictive control (MPC), and optimal control techniques such as trajectory optimization and model predictive path integral (MPPI)
• Experience with simulation environments such as PyBullet, MuJoCo, or IsaacLab
• Experience with bi-manual robotic manipulation hardware and force sensing
• Experience using ROS2 and Python

People have always been at the heart of our work. Our people are at the centre of the University community and everything we do. We value colleagues with drive, determination and a passion for what they do. We are a place where everyone is welcome and offer a range of policies and benefits designed to support you in building the right meaningful flexibility that works for you.

A career with us has a range of other benefits that can be tailored to your lifestyle:  

• Opportunities to develop new skills and broaden your experience    
• Leading-edge research  
• Opportunities for publication  
• Responsibility and autonomy  

This post is full-time; however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.

Apply Before: 22/09/2026, 23:59

£41,064 to £48,822 per annum, pro rata if part time

The University of Edinburgh is a world-class organisation. We look for the best in the field across all disciplines and provide a working environment where academics can develop their careers and passion for their chosen subject area. We offer the full range of academic roles and have a genuine focus on our student’s performance and wellbeing.

The Role

As research associate, you will conduct research within the Statistical Machine Learning and Motor Control (SLMC) group, part of the Institute of Perception, Action and Behaviour (IPAB). You will support the team in advancing the state of the art in whole-body, multi-contact robotic manipulation, developing and combining model-based methods (e.g. trajectory optimization, MPPI) with machine learning approaches (e.g. RL, diffusion policies) to scale and deploy multi-contact robot motions.

You will work with robotic manipulation hardware and sensing, including KUKA LBR robot arms and Kawada's Nextage NXA, and contribute to shared-autonomy methods for skilled teleoperation of robot manipulators. The role also involves collaboration and field deployment trips with project partners in Japan, alongside regular reporting on progress and results.

Desirable knowledge and experience:

• Experience mentoring undergraduate and master's students on research projects related to this research area
• Experience in robot learning techniques, such as reinforcement learning (RL) and/or flow matching and diffusion policies
• Experience in motion planning, model predictive control (MPC), and optimal control techniques such as trajectory optimization and model predictive path integral (MPPI)
• Experience with simulation environments such as PyBullet, MuJoCo, or IsaacLab
• Experience with bi-manual robotic manipulation hardware and force sensing
• Experience using ROS2 and Python

People have always been at the heart of our work. Our people are at the centre of the University community and everything we do. We value colleagues with drive, determination and a passion for what they do. We are a place where everyone is welcome and offer a range of policies and benefits designed to support you in building the right meaningful flexibility that works for you.

A career with us has a range of other benefits that can be tailored to your lifestyle:  

• Opportunities to develop new skills and broaden your experience    
• Leading-edge research  
• Opportunities for publication  
• Responsibility and autonomy  

This post is full-time; however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.

Apply Before: 22/09/2026, 23:59

£41,064 to £48,822 per annum, pro rata if part time