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

Delft University of Technology; Delft; Sur place; EUR 28 000 - 39 000; Plein temps

Résumé du poste

Delft University of Technology in Delft, Netherlands, invites applications for an MSc research position on safe planning and decision-making for robots within the EU OPERA project. The role emphasizes theory, simulation, and real-robot validation in collaboration with international partners.

The ideal candidate holds an MSc in Systems and Control, Robotics, or Applied Mathematics, and has strong programming skills in C++ or Python, with experience in ROS/ROS 2.

Qualifications

• An 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 with validation in simulation and on real robots.
• Strong analytical and mathematical skills in optimization, probability and statistics, and dynamical systems.
• Excellent communication skills and proficiency in English (written and verbal).
• Hands-on experience implementing optimization-based planners or controllers (e.g., MPC 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.

Connaissances

Optimization

Probability & statistics

Dynamical systems

C++

Python

English communication

ROS / ROS 2

Reinforcement learning

Uncertainty quantification

Formation

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

Outils

CasADi

acados

Description du poste

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.

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.

A letter of motivation explaining why you are the right candidate for this project

An updated cv Exam transcipts from your Bachelor and Master

A copy of your MSc thesis, or draft MSc thesis (or a link to access it)

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