Doctoral student position in data-driven geometric methods for robotics at KTH Royal Institute of Technology, Stockholm, Sweden
Application Deadline 8th August 2022
In this project, we will develop simplicial complex-based robot configuration space models for embodiment-aware robot motion planning and learning from demonstration. We will develop extremely large scale and detailed data-driven simplicial complex-based models of robot arm configuration spaces that represent the complex geometry that they exhibit. Based upon these representations we will develop algorithms for efficient updates, optimal motion planning and probabilistic reasoning for robotic manipulation. The project will be highly interdisciplinary as it lies at the intersection of Computational Geometry, Motion Planning, Bayesian Machine Learning and Optimal Control.
The doctoral student will be supervised by Associate Professor Florian Pokorny
The position is at the division of Robotics, Perception and Learning (RPL) at KTH.
For more information feel free to reach out to Associate Professor Florian Pokorny, firstname.lastname@example.org
For more information on our research, please visit
and you can apply here: