Join the Wingtra team and become part of this venture-backed robotics scaleup with a global and international team of 125+ dedicated Wingtranauts who want to see their actions have a positive and lasting impact on the world. Founded more than 8 years ago at ETH Zurich, Europe's leading robotics university, our goal is to build the best aerial robots to digitize the world at the push of a button and set the basis for faster and better decisions. Wingtra provides efficient and reliable data solutions to a variety of industries ranging from mining, construction and agriculture to humanitarian organizations, environmentalists and wildlife monitoring groups.
We are reaching for the stars and together we might just get there. Open communication, asking hard questions and valuing diverse viewpoints are but a few things that will help us achieve our goals. Above all we will never stop learning and striving to help each other reach our maximum potential.
OVERVIEW Tailsitter VTOL drones operate across hover and forward-flight regimes using the same aerodynamic surfaces and shared drivetrains for both hover and cruise. This coupling creates challenging flight conditions during mode transitions and makes landing and takeoff sensitive to wind gusts and ground-effect disturbances. Furthermore, using the same propulsion system for both power-hungry hover and efficient cruise restricts the optimization of the drivetrain.
This thesis explores enhanced control-effectiveness concepts to increase hover control authority and improve cruise efficiency in fixed-wing VTOL aircraft. The study combines modeling, control allocation, high-fidelity simulation, and experimental validation on an existing platform.
GOALS
• Extend and refine the simulation environment to enable rapid benchmarking of algorithms.
• Design and experiment with control allocation and flight control techniques ranging from classical optimization-based to adaptive and learning-augmented approaches.
• Validate the new control concept and compare it with the state of the art in flight performance, endurance, agility, and wind robustness.
• Validate results in simulation and real flights on Wingtra drones.
SUPPORT AND INFRASTRUCTURE Wingtra provides access to hardware, flight platforms, simulation environments, and engineering expertise to enable rapid development and safe experimental validation.
During the project, you are part of the Wingra team - experience the work environnement in a Robotics scale-up / ETH spin off, with global success and experience in the real-world UAV industry.
Student Requirements
* Background in control theory (nonlinear control advantageous), recursive estimation, and optimization.
* Experience with UAV dynamics or robotics modeling.
* Proficiency in C++ and Python; familiarity with PX4 beneficial.
* Motivation to conduct structured experimental work.
* Additionally to the Master thesis, a (paid) work student engagement with Wingra can be discussed.
* EU citizenship or enrollment in a Swiss institution required to comply with work-permit regulations.
If you enjoy teamwork more than being the individual superhero, and if you thrive in a feedback-driven culture and an exciting, unconventional yet structured and progressive start-up environment, we would love to hear from you. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.