PhD Position: Safe Planning and Decision-Making under Uncertainty for Long-Horizon Robot Autonomy

Delft University of Technology (TU Delft); Delft; Vor Ort; EUR 34.000 - 43.000; Vollzeit

Zusammenfassung

Delft University of Technology (TU Delft) invites applications for a PhD position within the OPERA consortium to advance general-purpose AI for robotics. You will develop planning and decision-making methods that enable safe, long-horizon robot behavior in uncertain environments. The PhD is a 4-year appointment in the Cognitive Robotics Department, supervised by Prof.

Robert Babuška and Dr. Laura Ferranti, with collaboration across Europe.

Qualifikationen

• MSc degree in Systems and Control, Robotics, Applied Mathematics, or a closely related field.
• Strong interest in safe planning and decision-making for robots, at the intersection of planning, control, and learning.
• Rigorous, theoretically grounded research, combined with an eagerness to validate ideas in simulation and on real robots.
• Strong analytical and mathematical skills, in particular in optimization, probability and statistics, and dynamical systems.
• Excellent communication skills and proficiency in English (written and verbal), as required for academic publication and international collaboration.

Aufgaben

• Develop planning and decision-making methods that let robots act safely and reliably over long time horizons when knowledge of environment and people is incomplete.
• Formulate the methods and analyze their guarantees and failure modes.
• Collaborate with OPERA partners across Europe on simulation, perception, and the OPERA open-source toolbox.
• Work within the Cognitive Robotics Department at TU Delft under supervision of Prof. Robert Babuška and Dr. Laura Ferranti.

Kenntnisse

Control theory

Optimization

Motion planning

English proficiency

Ausbildung

MSc in Systems and Control, Robotics, Applied Mathematics, or closely related field

Jobbeschreibung

Join TU Delft and the EU-funded OPERA consortium. Push the frontier of general-purpose AI for robotics and tackle the fundamental tension between long-horizon reasoning and real-time safety guarantees.

Job description

Robots that operate for long periods among people must plan beyond the next few seconds while staying safe. They need to predict how their surroundings and the people in them will evolve, know how reliable these predictions are, and adapt their plans when the predictions fail. In the EU-funded OPERA project, TU Delft contributes to General-Purpose AI for robotics by developing methods that combine fast, reactive behavior with more deliberate reasoning, planning, and decision-making.

In this PhD position, you will develop planning and decision-making methods that let robots act safely and reliably over long time horizons when their knowledge of the environment and of the people around them is incomplete. The research sits at the intersection of model-based planning and control and learning-based models, and addresses the trade-off between safety, performance, and real-time computation. You will formulate the methods and analyze their guarantees and failure modes. This research is connected to OPERA's tasks on GPAI-powered planning and decision-making, on long-term reasoning in human-centered environments, and on adaptive human-robot and robot-environment interaction.

Depending on your background, the work may draw on optimal control and model predictive control, motion planning, probabilistic modeling and estimation, or learning-based methods. The emphasis is on methods with a clear formulation and well-understood failure modes, validated in simulation and on real robots.

You will work in the Cognitive Robotics Department at TU Delft under the supervision of Prof. Robert Babuška and Dr. Laura Ferranti, and you will be embedded in the Reliable Robot Control Lab (https://r2clab.com/). You will collaborate with OPERA partners across Europe on simulation, perception, and the OPERA open-source toolbox.

Job requirements

The ideal candidate for this PhD position has a strong background in control, optimization, or motion planning and is motivated by the question of how robots can plan safely and reliably over long time horizons. We welcome applicants from all backgrounds who want to work at the intersection of planning, control, and learning.

• An MSc degree in Systems and Control, Robotics, Applied Mathematics, or a closely related field.
• A strong interest in safe planning and decision-making for robots, at the intersection of planning, control, and learning.
• A passion for rigorous, theoretically grounded research, combined with an eagerness to validate ideas in simulation and on real robots.
• Strong analytical and mathematical skills, in particular in optimization, probability and statistics, and dynamical systems.
• Excellent communication skills and proficiency in English (written and verbal), as required for academic publication and international collaboration.

You are particularly encouraged to apply if you have experience in one or more of the following areas:

• Hands-on experience implementing optimization-based planners or controllers (e.g.,model predictive control with acados or CasADi) in C++ or Python.
• Safe or robust control and planning under uncertainty.
• Learned dynamics or prediction models, model-based reinforcement learning.
• Uncertainty quantification, probabilistic or Bayesian modeling, state estimation.
• Long-horizon or hierarchical planning, prediction of human or multi-agent behavior.
• Real-robot deployment with ROS or ROS 2.

As part of OPERA, you will travel to meet and collaborate with the project’s European partners and attend regular consortium meetings.

We particularly encourage applications from women and other underrepresented groups, as we are committed to building a diverse and inclusive research environment.

TU Delft (Delft University of Technology)

Working at TU Delft means contributing to solutions that really make a difference.

For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future.

At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional. Working at TU Delft means join an international community of professionals and students. Together, we create knowledge, innovations, and solutions that help move the world forward.

Faculty Mechanical Engineering

From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.

ME is a dynamic and innovative faculty with high-tech lab facilities and international reach. It’s a large faculty but also versatile, so we can often make unique connections by combining different disciplines. This is reflected in ME’s outstanding, state-of-the‑art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well‑being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation.

Conditions of employment

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from€3059 - €3881 gross per month, from the first year to the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end‑of‑year bonus of 8.3%.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.

Additional information

For more information, please reach out to Laura Ferranti and Robert Babuska

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.

• #EUfunded This is an EU funded project, named OPERA, with project number 101298363, within program HORIZON/HORIZON-CL4-2025-04-DIGITAL-EMERGING-07
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