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TECNALIA PhD Grant 2019: Development of advanced control algorithms for the electric aircrafts of the future

About This PhD Project

Project Description

This grant focuses on the development of new advanced control algorithms to lay the foundation for an essential development topic (aircraft dynamics, cooperative control or fleet control and safe communications), contributing to new solutions that produce added value against the current state of the art.

This grant encompasses a scientific and an experimental part, allowing to safely test the advanced control algorithms on small-size aircraft or aerial platforms.

The Ph.D. student will enter the TEA Lab (TECNALIA ELECTRIC AIRCRAFT Lab) executing her/his duties next to the researchers of the TEA within a multicultural, dynamic and enriching work environment.

TECNALIA ELECTRIC AIRCRAFT Lab (TEA) works to foster the welfare of society by means of a safer and more ecological air transport. The boom of electric-propulsion-based new innovative aircraft architectures is oriented towards the transportation of goods, people or even services and will be integrated into the future Urban Air Mobility. This, along with a global trend shared with different countries around the world to facilitate the validation of concepts by means of flying prototypes, creates an unbeatable context for the development and research on the electric sustainable and safe air transport of the future.

For that purpose, we are setting up a group involving different areas of expertise devoted to devising the air transport of the future.

We’re a highly-qualified team that’s passionate about what it does and where it wants to go, and you can expect to work in a start-up like atmosphere and performing tasks that are diverse challenging and enriching by nature.

Join the TEA Lab and start creating the air transport of the future!


Previous experience of the applicant in the topics of the thesis (to master level). Knowledge of traditional and machine-learning-based control strategies, as well as automatic learning, will be positively valued. Proactivity, critical analysis and team and individual working skills will be an advantage. Curiosity for learning and the capability of approaching new complex problems with interest and without fear are the most valued assets.

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