Magnet.me - Het slimme netwerk waar studenten en professionals hun stage of baan vinden.
Het slimme netwerk waar studenten en professionals hun stage of baan vinden.
Je carrière begint op Magnet.me
Maak een profiel aan en ontvang slimme aanbevelingen op basis van je gelikete vacatures.
Mastering varying radar waveform settings in training sets for deep neural networks
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.
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.
You want an internship opportunity 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 work placement and be given the scope for you to get the best out of yourself. Furthermore, we provide:
At TNO, we innovate for a healthier, safer and more sustainable life. And for a strong economy. Since 1932, we have been making knowledge and technology available for the common good. We find each other in wonder and ingenuity. We are driven to push boundaries. There is all the space and support for your talent and ambition. You work with people who will challenge you: who inspire you and want to learn from you. Our state-of-the-art facilities are there to realize your vision. What you do at TNO matters: impact makes the difference. Because with every innovation you contribute to tomorrow’s world.
At TNO we encourage an inclusive work environment, where you can be yourself. Whatever your story and whatever unique qualities you bring to the table. It is by combining our unique strengths and perspectives that we are able to develop innovations that make a real difference in society.
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.
Resources:
Change language to: English
Deze pagina is geoptimaliseerd voor mensen uit Nederland. Bekijk de versie geoptimaliseerd voor mensen uit het Verenigd Koninkrijk.