ST0231: Internship - Radar-based Perception and Generation
About The Position
The Computational Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in radar-based perception (detection, tracking, pose/shape, segmentation) and generation (e.g., waveform/signal synthesis, differentiable radar simulators, dynamic scene generation). Previous hands-on experience with open indoor and outdoor radar datasets is a plus. Familiarity with basic radar concepts and MERL's recent work in radar perception is an asset. The intern will work closely with MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The internship is expected to last 3 months.
Requirements
- Solid understanding of state-of-the-art perception and generation frameworks including transformer-based (e.g., DETR), diffusion-based (e.g., DiffusionDet), and hybrid neural-physics pipelines.
- Hands-on experience with open large-scale radar datasets such as MMVR, HIBER, RT-Pose, HuPR.
- Proficiency in Python and experience with job scheduling on GPU clusters using tools like Slurm.
- Proven publication records in top-tier venues such as CVPR, ICCV, ECCV, NeurIPS.
- Knowledge of basic radar concepts such as FMCW, MIMO, (micro-) Doppler signature, radar point clouds, heatmaps, and raw ADC waveforms.
- Familiarity with MERL's recent radar perception research such as TempoRadar, SIRA, MMVR, RETR, and RAPTR.
Nice To Haves
- Previous hands-on experience with open indoor and outdoor radar datasets is a plus.
- Familiarity with basic radar concepts and MERL's recent work in radar perception is an asset.
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What This Job Offers
Career Level
Intern
Education Level
No Education Listed
Number of Employees
5,001-10,000 employees
