[Jobs] PhD and Postdoc Positions in Reinforcement Learning at Aalto University, Finland

[jon...@gmail.com]

TOPIC 1. We offer a PhD student and Postdoc position for developing reinforcement learning algorithms with mobile manipulation as an application:
https://www.aalto.fi/en/open-positions/phd-student-position-in-reinforcement-learning-and-mobile-manipulation
https://www.aalto.fi/en/open-positions/postdoc-position-in-reinforcement-learning-and-mobile-manipulation

TOPIC 2. We offer a PhD student position for developing multi-agent reinforcement learning and planning algorithms:
https://www.aalto.fi/en/open-positions/phd-student-position-in-multi-agent-reinforcement-learning-and-planning-0


BACKGROUND:

The Aalto Robot Learning research group operates in the intersection of artificial intelligence and robotics. We focus on developing methods for reinforcement learning, robotic manipulation, decision making under partial observability, imitation learning, and decision making in multi-agent systems. The goal of the research group is to help robots understand what they need to learn to perform their assigned tasks, and, thus, make robots capable of operating on their own and pro-actively help humans. To accomplish these goals the research group develops novel decision-making methods and uses these methods to solve unsolved robotic tasks. For more information, please see https://rl.aalto.fi .

The main task will be to develop new reinforcement learning and planning methods. The developed methods may be based on one of our focus areas improving efficiency and generalization in control of complex systems. The exact direction of the research is chosen depending on your experience and interests. Please relate clearly to the research topics in your Letter of Motivation.

In TOPIC 1, a goal is to develop methods that can be used for robotic mobile manipulation in unstructured partly unknown environments and in environments with elastic or deformable objects. The developed methods are evaluated with real robots such as our Boston Dynamics Spot robot.

In TOPIC 2, example research topics include but are not limited to multi-agent curriculum learning, hierarchical multi-agent reinforcement learning, model-based multi-agent reinforcement learning, and multi-agent planning and control. The developed methods may be evaluated with real robots.


Best wishes,
Joni

Joni Pajarinen,
Associate Professor, Robot Learning, Department of Electrical Engineering and Automation, Aalto University, Finland
https://rl.aalto.fi