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

Doctorat / PhD Type de contrat : FULL_TIME Date limite de candidature : 2026-11-15T23:59:59+01:00

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. You have: 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…