TU Delft

Postdoc: Safe Reinforcement Learning for Motion Planning: Theory

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Faculty Mechanical, Maritime and Materials Engineering

The 3mE Faculty trains committed engineering students, PhD candidates and post-doctoral researchers in groundbreaking scientific research in the fields of mechanical, maritime and materials engineering. 3mE is the epitome of a dynamic, innovative faculty, with a European scope that contributes demonstrable economic and social benefits.


The main focus of the Cognitive Robotics department is the development of intelligent robots and vehicles that will advance mobility, productivity and quality of life. Our mission is to bring robotic solutions to human-inhabited environments, focusing on research in the areas of machine perception, motion planning and control, machine learning, automatic control and physical interaction of intelligent machines with humans. We combine fundamental research with work on physical demonstrators in areas such as self-driving vehicles, collaborative industrial robots, mobile manipulators and haptic interfaces. Strong collaborations exist with cross-faculty institutes TU Delft Robotics Institute and TU Delft Transport Institute), our national robotic ecosystem (RoboValley, Holland Robotics) and international industry and academia. 

Functie omschrijving

Reinforcement learning (RL) is showing increasing potential in real-world robotic applications, however, theoretical guarantees for RL such as stability, robustness and safety are still missing. The goal of this project is to close the gap between control theory and black-box RL methods and produce a stability-guaranteed RL method with a focus in home robotics applications. The PostDoc will explore how to guarantee the stability properties in RL for the general class of nonlinear stochastic systems. In this project, we will collaborate with AnKobot to develop novel smart and safe cleaning robots’ concepts. 

Functie eisen

The candidate is expected to be able to explore tools in both control theory, machine learning, stochastic process and probability theory. Thus, we are looking for a candidate with a PhD degree in systems and control, robotics, applied mathematics, artificial intelligence, machine learning, or a related subject. The candidate must have strong analytical skills and must be able to work at the intersection of several research domains. Experience with study on stochastic process is a plus. Experience with real robot applications is also a plus. The applicant should have demonstrated ability to conduct high-quality re-search according to international standards, as demonstrated by publications in international, high-quality journals. A very good command of the English language is required, as well as excellent communication skills. 


TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.   

Informeren en solliciteren

If you have specific questions about this position, please contact Dr. Wei Pan, e-mail: wei.pan@tudelft.nl. Please do not send application emails here but use the specified address below.

To apply, please prepare:

  1. a letter of motivation explaining why you are the right candidate for this project,

  2. a detailed CV,

  3. electronic copies of your top three publications,

  4. the names and contact addresses of two or three references.

All these items should be combined in one PDF document. Applications should be submitted by email at the earliest convenience to application-3mE@tudelft.nl. When applying for this position, please refer to vacancy number 3mE19-112.

The review of applications will start on January 15, 2020 and continue until the position is filled. The intended starting date is as soon as possible.

Acquisitie naar aanleiding van deze vacature wordt niet op prijs gesteld.

Uren per week: 38.0 uur per week

Salaris: € 3.255,- tot € 4.274,- per maand


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