Internship | Recurrent deep learning applied to radar data
Exploiting recurrent deep learning techniques for exploitation of temporal structures in radar data
Education typeuniversity (wo)
TypeInternship and graduation project
Hours a weekFulltime – 40
What will be your role?
The goal of this assignment is to investigate whether this temporal structure can be exploited to improve the suppression of radar clutter and interference, for instance by predicting the clutter or interference for the next radar observation. Then the next question would be: is it also possible to adapt to (slow) changes of the temporal structure? For example, the blades of a wind turbine tend to rotate faster when the wind increases, can this change be taken into account in the subsequent predictions? One potential line of investigation is the use of deep learning techniques with a recurrent structure.
You will perform this assignment in the Department of Radar Technology. We are a passionate and creative group of professionals (60 people) dedicated to the specification, development and evaluation of innovative, high-performance MMICs, miniaturised and integrated RF subsystems, antennas and front-ends. The department is at the heart of novel, game-changing radar system and signal processing concepts for the military, space and civil domains.
How do you want to contribute to tomorrow's world? How big can your impact be? Come and work at TNO and envision it.
What we expect from you
What you’ll get in return
TNO as an employer
The selection process
For this vacancy it is required that the AIVD issues a security clearance after conducting a security screening. Please visit for more information the AIVD website.
Has this job opening sparked your interest?
Then please feel free to apply on this vacancy! For further questions don’t hesitate to contact us.
Due to Covid-19 and the consequent uncertainties and restrictions, students who are not residing in the Netherlands may currently not be able to start an internship or graduation project at TNO.
Contact: Jacco Wit, de
Phone number: +31(0)88-86 61057