"Postdoc Bayesian Reinforcement Learning for Learning to Collaborate with Humans"

Job description

Are you passionate about AI and do you want to advance the frontier of human-AI collaboration?

We are seeking a motivated postdoctoral researcher to join our team and explore the exciting intersection of Bayesian reinforcement learning, deep learning and human-AI interaction. This position focuses on developing adaptive algorithms that enable AI agents to learn, reason, and collaborate effectively with human partners in dynamic, uncertain environments. By integrating Bayesian reasoning you will address the sample complexity of reinforcement learning, making it applicable to human-AI collaboration settings.

Why build AI systems that replace people if we can build AI systems that collaborate with people? Hybrid Intelligence (HI) is the combination of human and machine intelligence, expanding human intellect instead of replacing it. Our goal is to design Hybrid Intelligent systems, an approach to Artificial Intelligence that puts humans at the center, changing the course of the ongoing AI revolution.

At Delft University of Technology we are looking for a postdoc who wants to push machine learning beyond traditional settings that assume a fixed dataset. Specifically, collaborating with people requires both reasoning about the sequential aspects of the interaction, as well as adapting based on very few data points. Many reinforcement learning methods, however, require a very large set of experience. We aim lower this sample complexity, by building exploring reasonable assumptions (e.g., from cognitive psychology) on goals and behaviors of humans, thus making an important step towards AI that can effectively adapt to its human collaborator.

The postdoc will be based in the sequential decision making group at the department of intelligent systems, and will benefit from the network provide by the ELLIS Unit Delft.

Additionally, this postdoc position is part of the Hybrid Intelligence project and the successful candidate will be supervised by Dr. Frans Oliehoek (TU Delft) and Dr. Jan Willem van de Meent (University of Amsterdam). The successful candidate will attend project meetings and actively collaborate with a variety of project partners.

Tasks and responsibilities:

• Conducting independent research in reinforcement learning, with a focus on human-AI interaction.
• Disseminating research outcomes through publications, conferences, and workshops.
• Actively contributing to the larger research project goals of Hybrid Intelligence
• Contributing to education, e.g. by supervising bachelor or master theses.

Job requirements

Strict requirements:

• A PhD degree in AI or closely related topics in computer science, math, or physics.

Other desiderata:

• Thorough knowledge of general area of reinforcement learning, decision making under uncertainty, and/or other forms of interactive machine learning.
• Track record with international publications.
• Good coding skills and experience in contemporary machine learning frameworks (e.g., Pytorch).
• Fluent in English
• Self-motivated
• Team player: willing to initiate collaborations with other partners in the project.

Conditions of employment

• Duration of contract is 13 months
• Temporary
• A job of 28,5-40 hours per week.
• Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
• An excellent pension scheme via the ABP.
• The possibility to compile an individual employment package every year.
• Discount with health insurers on supplemental packages.
• Flexible working week.
• Every year, 232 leave hours (at 38 hours). You can also sell or buy additional leave hours via the individual choice budget.
• Plenty of opportunities for education, training and courses.
• Partially paid parental leave
• Attention for working healthy and energetically with the vitality program.

Additional information

If you would like more information about this vacancy or the selection procedure, please contact Prof. Dr. Frans Oliehoek, via f.a.oliehoek@tudelft.nl.

Application procedure

Are you interested in this vacancy? Please apply no later than 1 March 2026 via the application button and upload the following documents:

• CV
• Motivational letter

You can address your application to Prof. Dr. Frans Oliehoek.