PhD student on “Beyond Kalman Neural Network-controlled Smart Digital Filters

Department(s)

Electrical Engineering

Graduate Program(s)

Electrical Engineering

Reference number

V36.5504

Signal processing for signal enhancement and interference rejection is a field with a long history and has become ubiquitous in modern-day life. Without these signal processing solutions, we would not be able to communicate with each other via zoom, listen to the radio when we are stuck in traffic, or enable the hearing-impaired to hear again and participate in society. Clearly, across all of these applications it is pivotal to perform processing in real-time and with low-latency, while data streams are very high-rate and ever expanding. Simultaneously, the complexity of the problem is growing as the environment in which these solutions are deployed is becoming ever more hostile and challenging, with many concurrently used devices and systems that interfere with each other.

Signal processing algorithms are classically derived based on statistical models of the environment, which in turn are derived from first principles. To retain real-time processing rates and mathematical tractability, such solutions are restricted in their complexity and commonly assume naive signal properties (both in terms of the measurement likelihood models and signal priors). This greatly limits performance, and upper bounds progress on advanced algorithms for future systems.

In this project, we aim to lay the foundations for future systems that break this upper bound, by combining principled modelling with rapid inference under data-driven statistical models. Through deep neural augmentation we strive to overcome the shortcomings of today’s signal processing solutions by learning from real-world data, while carefully balancing complexity and accuracy to retain real-time performance, ultimately leading to real hardware implementations.

To help drive this development TU/e has teamed up with NXP, a globally leading signal processing IC design company, in a joint research program. In this program students will be co-supervised by top AI experts from TU/e and top real-time algorithm and IC-design experts from NXP. Students will also be hosted by NXP on a part-time basis. As a result, the positions in the program offer a unique combination of high-quality scientific and industrial experience.

The following position in this program is still available.  For it  we are looking for talented, team-working-oriented and inquisitive candidates with an electrical engineering background and strong signal processing or AI skills. Applications from computer science and AI MSc students with affinity for signal processing, sensing and hardware implementation are also welcomed. 

PhD position on smart digital filters for radio broadcast interference rejection in electrical vehicles

Interference is a common problem in wireless communications and is becoming more severe as the wireless spectrum becomes more crowded. Conventional interference rejection methods focus on radiated interferences, which originate from dedicated wireless transmitters. Recently, due to the fast development of electrical vehicles, a new vehicle-internal type of interference has emerged. For example, it is observed that DC/DC converters in electrical-cars introduce significant interference to radio receivers from the low-frequency (around 1 MHz) Amplitude Modulation (AM) signal-band all the way up to the Digital Audio Broadcasting (DAB) band (around 200 MHz). This new type of interference differs from the conventional interference in many respects. For example, it varies widely in time, frequency, and among different car models, and no statistical or deterministic interference models are available yet.

As such, this PhD position seeks to go all the way from characterizing and modeling these statistically complex interferences using generative AI models (e.g. normalizing flows, VAEs or score-based models) to developing and validating AI based techniques to suppress it. Also, here algorithmic complexity is an essential constraint, and complexity levels should be consistent with low-power real-time implementation.

For all of these positions we are looking for talented, team-working-oriented and inquisitive candidates with an electrical engineering background and strong signal processing or AI skills. Applications from computer science and AI MSc students with strong affinity for signal processing, sensing and hardware implementation are also welcomed. 

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation after one year.
• To support you during your PhD and to prepare you for the rest of your career, you will have free access to a personal development program for PhD students (PROOF program ).
• A gross monthly salary of € 2.443,00 in the first year and € 3.122,00 in the last year (on a fulltime basis). The salary is in accordance with the Collective Labor Agreement of the Dutch Universities.
• Benefits in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.

More information

Do you recognize yourself in this profile and would you like to know more? Please contact
prof.dr.ir. J.W.M. Bergmans: j.w.m.bergmans[at]tue.nl.

For information about terms of employment, please click here  or contact HR Services: hrservices.flux[at]tue.nl

Please visit www.tue.nl/jobs to find out more about working at TU/e!

Application

We invite you to submit a complete application by using the 'apply now'-button on this page.
The application should include:

• A cover letter in which you describe your motivation and qualifications for the position.
• A curriculum vitae, including a list of your publications and the contact information of three references.
• Copies of relevant BSc and MSc diplomas and grade transcripts.

We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.  

Applications by e-mail are not accepted!

Please keep in mind you can upload only 5 documents up to 2 MB each. If necessary please combine files.