Internship: Phase-Noise-Resilient Architectures for Large MIMO FMCW Automotive Radars
On-siteEindhoven, North Brabant, The Netherlands
Job Summary
Explore next-generation automotive radar architectures combining large-scale MIMO imaging with phased-array beam steering. Investigate phase noise accumulation and multipath propagation in systems employing DDMA and related techniques. Develop mathematical models and simulation frameworks to analyze performance metrics including angle resolution, Doppler resolution, and detection capability. Research hybrid radar architectures and evaluate beam-scanning strategies, sparse transmit activation methods, and subarray-based beamforming concepts. Design and perform MATLAB and/or Python simulations to compare alternative radar architectures and quantify system trade-offs. Deliver a literature review, simulation framework, and final technical report summarizing findings on array configurations and waveform design alternatives.
Required Qualifications
- Master's degree in Electrical Engineering, Signal Processing, Applied Mathematics, Physics, or a related technical discipline
- Strong interest in radar systems, wireless communications, signal processing, and sensing technologies
- Good understanding of FFT-based processing, range-Doppler processing, detection theory, estimation theory, array signal processing, and direction-of-arrival estimation
- Experience with MATLAB and/or Python for modeling, simulation, visualization, and algorithm development
- Self-motivated, curious, analytical, and comfortable working on open research questions with both independent and collaborative work styles
- Strong written and verbal communication skills, with the ability to document technical findings and present results clearly
Desired Qualifications
- Familiarity with FMCW radar, MIMO radar, phased arrays, beamforming, antenna arrays, and RF impairments such as phase noise
- Knowledge of structured software development practices and version control tools such as Git
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