Type dienstverband:
Internship and graduation project
Den Haag
Uren per week:
Fulltime – 40

Internship | Exploiting attention to streamline the training of deep neural networks applied to radar data

Experts giving attention to neural networks

What will be your role?

Deep learning techniques usually thrive when large sets of labelled data are available. In the radar domain, labelled data tend to be scarce depending on the application area. By exploiting expert knowledge it may be possible to improve the training process and overall performance when using only small labelled data sets. From experience an expert may know what features are more distinctive or less distinctive given the specified target classes. In addition, the characteristics of the background clutter in different radar measurements may vary. This can affect the training process, which is undesired if the background clutter does not contain information about the target class (as is typically the case). In these cases, an expert might be able to give the neural network a nudge in the right direction.

The goal of this assignment is to investigate what type of expert knowledge would be useful to streamline the training process and how this knowledge should be used in that process. For this assignment, visualisation techniques, highlighting the image pixels that are used for classification, can be a supporting tool. Subsequently neural attention can be applied to focus the training process on those image pixels that actually contain relevant information. Radar measurements of different target classes are available for this assignment.

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.

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 we’d like to hear from you! Please contact us for more information about the job or the selection process. To apply, please upload your CV and covering letter using the ‘apply now’ button.