Internship: Phase-Noise-Resilient Architectures For Large MIMO FMCW Automotive Radars (Automotive)

TL;DR

Internship: Phase-Noise-Resilient Architectures for Large MIMO FMCW Automotive Radars (Engineering): Researching and evaluating next-generation automotive radar architectures combining MIMO imaging and phased-array beam steering with an accent on phase-noise robustness and signal processing. Focus on developing mathematical models, performing simulations in MATLAB/Python, and optimizing system trade-offs for angular and Doppler resolution.

Location: Eindhoven, Netherlands (Onsite)

Company

hirify.global is a leading global semiconductor manufacturer focusing on automotive, industrial, and IoT solutions.

What you will do

• Investigate the impact of phase noise accumulation and multipath propagation in large MIMO FMCW radar systems.
• Develop mathematical models and simulation frameworks to analyze radar system performance.
• Research hybrid radar architectures combining MIMO imaging with directional phased-array transmission.
• Design and perform MATLAB and/or Python simulations to quantify system trade-offs.
• Explore beam-scanning strategies, DDMA-based transmission, and subarray-based beamforming.
• Produce a final technical report and presentation summarizing findings and recommendations.

Requirements

• Must be pursuing a Master's degree in Electrical Engineering, Signal Processing, Applied Mathematics, Physics, or a related technical discipline.
• Strong interest in radar systems, wireless communications, and sensing technologies.
• Understanding of FFT-based processing, range-Doppler processing, and direction-of-arrival estimation.
• Proficiency in MATLAB and/or Python for modeling, simulation, and algorithm development.
• Familiarity with FMCW radar, MIMO, phased arrays, and RF impairments like phase noise.
• Ability to document technical findings and present results clearly.

Nice to have

• Knowledge of structured software development practices and version control tools like Git.