The Department of Aerospace Engineering at UC3M is looking for highly motivated candidates for two PhD positions. We are now collecting expressions of interest: interested candidates should contact the supervisors of the position(s) they are interested in. Selected candidates will join the PhD Programme in Aerospace Engineering and will be supported by the supervisors in applying for PhD funding.
PhD position 1 – Flying Anemometers: From Drone Dynamics to Urban Wind Forecasts
Supervisors: Dr. Luigi Marra and Dr. Abolfazl Simorgh.
Urban wind flows are highly turbulent and spatially complex, posing significant challenges to safe and energy-efficient drone operations (urban inspection, delivery, emergency response, rooftop landing). Fixed anemometer networks offer limited coverage, while high-fidelity flow simulations are expensive and difficult to update in real time.
This PhD aims to develop a framework in which cooperative drone swarms act as mobile wind sensors, estimating local wind velocities from their flight dynamics without dedicated anemometers. These sparse measurements will be used to train and continuously update a reduced-order latent predictive model of urban wind flows, enabling real-time reconstruction and short-term forecasting. The resulting predictions will support cooperative, risk-aware and energy-efficient flight planning, gust-aware landing, no-fly zone identification and contaminant-dispersion forecasting, closing the sensing–prediction–control loop.
Work packages:
- WP1 – Drone-based wind sensing: local wind and its uncertainty from calibrated drone dynamic models and navigation, inertial and actuation data.
- WP2 – Drone-only latent model learning: geometry-aware latent neural operators trained on irregular drone observations, updated through data assimilation.
- WP3 – Wind forecasting and robustness: short-term 3D wind forecasts with uncertainty, tested under unseen inflows, gusts, noise and sparse coverage.
- WP4 – Cooperative active sensing and applications: multi-agent trajectory planning with Model Predictive Control (MPC) and multi-agent Reinforcement Learning (RL); wind-hazard maps, gust-aware landing and contaminant dispersion.
Keywords: drones / UAS, urban air mobility, wind estimation, reduced-order modelling, neural operators, data assimilation, MPC, multi-agent reinforcement learning.
Desirable background: fluid mechanics/aerodynamics, flight dynamics and control, machine learning; programming skills (e.g. Python).
If you are interested in applying for this position, please contact professors Luigi Marra (lmarra@pa.uc3m.es) and Abolfazl Simorgh (asimorgh@pa.uc3m.es).
PhD position 2 – Propeller-Driven Vibration Testing for the Structural Health Monitoring of Multi-Material Wing Structures
Supervisors: Dr. Alberto Racionero Sánchez-Majano and Dr. Gabriele Dessena.
The pursuit of aerodynamic efficiency and mass reduction has shifted the design of lifting surfaces, such as wings and wind turbine blades, towards high-aspect-ratio structures made of high-specific-strength materials, typically fibre-reinforced composites. In multi-material structures, damage develops through several mechanisms and at different material scales, including fatigue, ductile or brittle fracture and debonding. This complexity makes the source and type of failure difficult to identify by standard visual inspection, while the lightweight wings of propeller-driven aircraft require monitoring during operation.
This PhD develops propeller-driven vibration testing (PVT), which uses the installed propeller as the sole excitation source, for the health monitoring of these wings. Closed-loop propeller excitation and automated modal identification are applied to a multi-material wing on the ground and in the wind tunnel. The identified modal parameters are then combined with a higher-order digital twin to locate and quantify damage, so that damage tolerance is verified in operation and maintenance is scheduled on condition.
Keywords: structural health monitoring, propeller-driven vibration testing, digital twin, damage localisation, wind tunnel.
Desirable background: structural dynamics and vibrations, composite structures, finite-element modelling, experimental testing and signal processing; programming skills (e.g. MATLAB/Python).
If you are interested in applying for this position, please contact professors Alberto Racionero Sánchez-Majano (alracion@ing.uc3m.es) and Gabriele Dessena (gdessena@ing.uc3m.es).
Requirements common to both calls
- A minimum of 300 ECTS (Bachelor + Master), as required for admission to the UC3M PhD Programme in Aerospace Engineering.
- Master’s degree in Aerospace/Aeronautical Engineering, Mechanical Engineering, Robotics/Control, Physics, Applied Mathematics or a related field.
- Strong academic record and genuine motivation for research.
- Good command of English (written and spoken).
- Ability to work both independently and as part of a multidisciplinary team.
How to express your interest
Send an email to the supervisors of the position you are interested in (contacts above), indicating “PhD 1 – Flying Anemometers” or “PhD 2 – PVT for SHM” in the subject, and attach:
- Your CV.
- Academic transcripts (Bachelor and Master).
- A short motivation letter (max. 1 page).
Deadline for expressions of interest:
Send your expressions of interest no later than November 15th. Early applications are encouraged.
Why UC3M?
Universidad Carlos III de Madrid (UC3M) is an innovative public university, ranked as the best young university in Spain and among the top young universities worldwide (QS Top 50 Under 50). The Department of Aerospace Engineering has been accredited as Unit of Excellence “María de Maeztu” by the Spanish State Research Agency (2026–2031). PhD candidates benefit from a high-quality research environment, state-of-the-art facilities, an international team and close mentoring throughout their PhD.
