What will be your role?
Nowadays, deep learning techniques are used in a wide variety of domains, including the radar domain. In the radar domain, deep learning is primarily applied for classification based on some 2D representation of the radar data, e.g., an Inverse Synthetic Aperture Radar (ISAR) image or a spectrogram (i.e., an image of signal frequency content versus time). These intermediate data representations are commonly used because they are easily interpretable for humans. Depending on the final application, however, deep neural networks might be trained more efficiently using other data representations. For specific applications it might even be beneficial to omit the intermediate data representation and train a neural network using pulse-compressed radar data or directly raw radar data.
The focus of this assignment is the use of pulse-compressed or raw radar data for training a deep neural network. What is the potential of this approach? What are possible applications for which this approach has added value? For instance, could pulse compression, beamforming and/or detection be combined in a single neural network and if so what is the benefit? Radar measurements are available for testing different approaches.
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
You are in the final stages of your degree in artificial intelligence, computer science, physics, mathematics, electrical engineering or a similar degree and have some track record in the field of signal processing or computer vision. You have experience in programming in Matlab and/or Python, you are pragmatic and focused on making things work. Next to technical expertise we value communication skills and a results-driven attitude.
What you’ll get in return
You want to work on the precursor of your career; an internship gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your internship and be given the scope for you to get the best out of yourself. Naturally, we provide suitable internship compensation.
TNO as an employer
TNO is an independent research organisation whose expertise and research make an important contribution to the competitiveness of companies and organisations, to the economy and to the quality of society as a whole. Innovation with purpose is what TNO stands for. With 3000 people we develop knowledge not for its own sake but for practical application. To create new products that make life more pleasant and valuable and help companies innovate. To find creative answers to the questions posed by society. We work for a variety of customers: governments, companies, service providers and non-governmental organisations.
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
Note that applications via email and third party applications are not taken into consideration.
Phone number: +31(0)88-86 61057