What will you be doing?
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 do we require of 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 can you expect of your work situation?
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.
What can TNO offer you?
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.
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 vacancy 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.
Note that applications via email and third party applications are not taken into consideration.
Contact: Jacco Wit, de
Phone number: +31(0)88-86 61057