Magnet.me  -  Het slimme netwerk waarop hbo‑ en wo‑studenten hun baan of stage vinden.

Het slimme netwerk waarop hbo‑ en wo‑studenten hun baan of stage vinden.

Deze vacature is verlopen. Je kunt daarom niet meer liken of solliciteren.

Vergelijkbare vacatures bekijken

Stage | Training of deep neural networks using radar measurements made with vary

Geplaatst 24 mrt. 2024
Werkervaring
0 tot 1 jaar
Full-time / part-time
Full-time
Functie
Soort opleiding
Taalvereiste
Engels (Vloeiend)

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WHAT WILL YOU BE DOING?

Deep learning techniques typically thrive when large sets of labelled data are available. In the radar domain, labelled data tend to be scarce depending on the application area. So it would be beneficial if all available data could be used. However, in practice measurements may be made with varying radar waveform settings (such as centre frequency, bandwidth, pulse repetition frequency), which has an impact on the representation of the information in the received signal. The unambiguous Doppler interval, for instance, depends on the pulse repetition frequency.

The goal of this assignment is to investigate how radar measurements made with varying waveform settings can be used for training a deep neural network for target classification. One possible approach is the investigation into the use of data representations that are independent of the radar waveform settings, such that the input to the neural network is consistent. Another possible approach is the investigation into neural network architectures that are robust with respect to varying waveform settings. Radar measurements with varying waveform settings 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.

Innovation with purpose: that is what TNO stands for. We develop knowledge not for its own sake, but for practical application. TNO connects people and knowledge to create innovations that boost the competitive strength of industry and the well-being of society in a sustainable way.

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