Safe autonomy of hybrid VTOL UAVs

Hybrid VTOL UAVs, i.e. platforms that combine the vertical take-off, landing, and hovering capability of multirotors with the efficient forward flight of fixed-wing aircraft, have matured significantly over the past decade. By uniting hover agility with long-range, energy-efficient cruise, they unlock missions that neither configuration can serve alone, including infrastructure inspection over large areas, precision agriculture, medical and logistics delivery, and search-and-rescue operations.

Despite these advances, the widespread deployment of hybrid VTOL UAVs remains constrained by the vulnerability of the hover-to-cruise transition to disturbances and actuator degradation, state estimation that deteriorates in GNSS-degraded or -denied conditions, planning frameworks that lack explicit risk awareness and contingency management, and a persistent dependence on human supervision. These limitations stand in the way of safe, certifiable operations beyond visual line of sight (BVLOS), as envisioned by emerging regulatory frameworks such as EASA's Specific Operations Risk Assessment (SORA) and the European U-space ecosystem.

This project outlines the conceptual and technical foundations for safe autonomy of hybrid VTOL UAVs, addressing these limitations through fault-tolerant transition-aware control, resilient multi-sensor state estimation, risk- and contingency-aware planning, and runtime assurance frameworks paving the way towards certifiable autonomy.

PhD Research Project

The objective of this PhD is to contribute to the safe autonomy of hybrid VTOL UAVs by advancing one or more of the following four research pillars:

- Design and implementation of fault-tolerant, transition-aware flight control for hybrid VTOL platforms, ensuring flight envelope protection and graceful degradation under actuator faults, wind gusts, and icing across the full hover-transition-cruise regime.

- Development of resilient state estimation through fusion of heterogeneous sensor modalities (IMU, GNSS, airspeed, vision, LiDAR), providing reliable localization and integrity monitoring in GNSS-degraded or -denied environments.

- Development of risk- and contingency-aware mission planning, including safety-aware path planning over populated areas, emergency landing site selection, geofencing, and reactive avoidance of static and dynamic obstacles.

- Development of runtime assurance and verification frameworks, combining online safety monitoring, formally bounded fallback behaviours, and certification-oriented evidence generation aligned with SORA and U-space requirements, reducing reliance on human supervision.

Validation of the developed systems is planned in controlled lab settings (drone cage, motion capture, hardware-in-the-loop simulation, onboard computing) and in outdoor flight campaigns, with application domains including long-range infrastructure inspection (e.g. pipelines, power lines, wind farms), precision agriculture (e.g. crop monitoring over large fields), logistics and medical delivery, and dual-use scenarios (e.g. maritime patrol and search-and-rescue).

The research is supervised by Prof. Jolan Wauters within the framework of an individual project on safe autonomy of hybrid VTOL UAVs. It offers close collaboration with the RAM division of KU Leuven, university association partners, international research partners, and industry partners.

We are seeking a highly motivated, enthusiastic, and communicative researcher with a proactive and creative attitude, eager to explore innovative solutions at the intersection of robotics, mechatronics, and autonomous systems. If you recognise yourself in the profile below, we encourage you to apply:

- I hold a Master’s degree in Mechanical Engineering, Electrical Engineering, Mechatronic Engineering, Robotics, Control, Computer Science, Aerospace Engineering, or a related field, and have performed above average relative to my peers.

- I am proficient in written and spoken English.

- I have gathered experience with one or more of the following: aerial robotics, flight dynamics and control, state estimation and sensor fusion, fault detection and diagnosis, motion planning, or safety verification of autonomous systems. Prior experience with PX4/ArduPilot, ROS, MATLAB/Simulink, or embedded hardware is a plus.

- I am proficient in Python and/or C++ and am familiar with simulation environments (e.g. Gazebo, MATLAB) and hardware-in-the-loop testing.

- As a PhD researcher at KU Leuven, I perform research in a structured and scientifically sound manner. I read technical papers, understand the nuances between competing methodologies, and implement and improve algorithms independently.

- Based on interactions with my supervisors and colleagues, I set up and regularly update a plan of approach for the upcoming 1 to 3 months toward my research goals. I work with a sufficient degree of independence and proactively flag deviations or items for discussion.

- In frequent reporting (weekly to monthly), I present my results clearly and give well-founded interpretations. I iterate on my work based on supervisor feedback and take ownership of my research direction.

- I am comfortable working as a team member and eager to share my results to inspire and be inspired by my colleagues.

- I value being part of a research group well connected to the mechatronics and aerospace industries, and am eager to learn how academic research can be linked to industrial innovation roadmaps.

- During my PhD I want to grow towards representing the research group at conferences, disseminating my work to an international audience, and contributing to a broader understanding of my research field.

We encourage candidates from diverse backgrounds and experiences to apply, as we believe that different perspectives contribute to better research and innovation.

Application Instructions for the PhD Vacancy

To apply for this position, please use the online application tool and submit the following documents in a single PDF file:

- Motivation Letter: A letter (maximum 1 A4 page) addressing your strengths and qualifications in relation to the project, and indicating which research pillar(s) align most closely with your background and interests.

- Complete Academic CV: A detailed CV including education, current position, work experience (if any), employment gaps (if any), interests, extracurricular activities, and projects demonstrating your programming skills and robotics/mechatronics background.

- List of Publications: If applicable, provide a list of your publications with DOIs. Please do not include PDFs.

- Copies of Diplomas: Include copies of your BSc and MSc degrees.

- Transcript of Records: Provide transcripts for your BSc and MSc. If your Master’s degree is not yet complete, include available credits, scores, and a list of courses in your upcoming semester.

- English Summary of Master Thesis: A summary in English (maximum 1 A4 page, or 2 pages when including a figure).

- Proof of English Language Proficiency: Documentation demonstrating your proficiency in English (TOEFL, IELTS, or equivalent), if available.

- Reference Letter or Contact Details: A reference letter or the contact information for one referee who can provide a recommendation letter upon request.