JOB DESCRIPTION

This groundbreaking project comprises four PhD positions, led by experts in radar technology, machine learning, and automotive engineering.
Radar images are often disturbed by noise, clutter, and interference, which sometimes leads to a misinterpretation of those radar images. Better understanding of the impact of noise and interference on radar analysis will lead to more robust interpretation of radar images and more reliable radar-based Advanced Driving Assistance Systems (ADAS) applications.

In the proposed project we have the following objectives:

• To use real and synthetic radar image datasets to train cutting-edge machine learning models for radar image interpretation, potentially yielding more reliable interpretations that are more resistant to radar clutter, clutter and interference, for both static and dynamic scenarios, where one or more target(s) need to be detected or followed.
• Rigorously assess and benchmark various existing and new ML approaches (neural networks and others) for the interpretation of radar data in the presence of radar clutter, noise and interference, for the case of static and dynamic scenarios, where one or more targets must be detected or tracked.

This PhD opportunity offers a stimulating and challenging experience, encompassing both theoretical and experimental aspects. It explores two cutting-edge disciplines— radar engineering and machine learning—within the context of Advanced Driving Assistance Systems.

JOB REQUIREMENTS

Must-haves:

• MSc in machine learning, electrical engineering, computer science, mathematics, or equivalent
• Strong analytical skills
• Proven record in programming skills
• Proficiency in English
• Good communication skills
• Ability to work in a multidisciplinary, highly dynamic environment

Nice-to-haves:

• Background in machine learning, radar technology, or automative engineering

Please refer the Job description for details