Doctoral student in Machine Learning
KTH Royal Institute of Technology, School of Computer Science and Communication
KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as in architecture, industrial management, urban planning, history and philosophy.
KTH Computer Science and Communication (CSC) announces PhD positions in Machine Learning at the department of Robotics, Perception and Learning (RPL) https://www.kth.se/en/csc/forskning/rpl.
The scientific work will be conducted along either of the following research directions:
1) Geometric and Topological Methods for Machine Learning with applications to Robotics
Topological Data Analysis is a recently emerging sub-branch of machine learning that enables inference about the global structure of datasets based on rigorous mathematical theory with origins in Algebraic Topology. The research under this theme will focus on developing new geometric and topological techniques for machine learning with a focus on potential application areas in robotics. Possible applications of Geometric and Topological Data Analysis in Robotics include reasoning about robot configuration spaces and free space. This could include development of approaches to autonomously detect unsafe configurations in a self-driving car scenario and to understand how compact components of the free space can be utilized to enable new types of robot manipulation interactions by means of the concept of “Caging”. A second potential sub-thread is to investigate how Topological Data Analysis may be of use to analyze representations of data determined by Deep Learning Algorithms. The research will be supervised by Florian Pokorny, Assistant Professor at RPL.
2) Data-driven scene understanding and control in Human-Robot collaborative settings
Future robotic applications are believed to a greater extent include collaboration with humans, humans that do not necessarily have a technical background. Interaction between human and robot thus need to be in a manner that the human finds natural. A collaborative robot should adapt to changes in the environment and tasks that are placed on it. It needs to be in constant learning mode and gradually adjust its behaviours given feedback from both its sensory system and the human collaborator. Research under this theme will focus on a combination of deep learning for scene understanding and generative action modelling, with reinforcement learning applied for control. Emphasis will be placed on methods with which a human can teach a robot the best way of solving a particular task, either through demonstration or by physically guiding the robot. The research will be supervised by Mårten Björkman, Associate Professor at RPL.
This is a four-year time-limited position that can be extended up to a year with the inclusion of a maximum of 20% departmental duties, usually teaching. In order to be employed, you must apply and be accepted as a doctoral student at KTH.