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ST0116: Internship - Deep Learning for Radar Perception
The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in radar perception. Expertise in deep learning-based object detection, pose estimation, segmentation, multiple object tracking (MOT), and representation learning on radar data is required. 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 with a preferred start date after June 2025.
Required Specific Experience
- Solid understanding of state-of-the-art perception frameworks including transformer-based (e.g., DETR) and diffusion-based (e.g., DiffusionDet) methods.
- Hands-on experience with open large-scale radar datasets such as MMVR, HIBER, RADIATE, and K-Radar.
- 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, and RETR.
- Research Areas: Computational Sensing, Signal Processing
- Host: Perry Wang
- Apply Now
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ST0096: Internship - Multimodal Tracking and Imaging
MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.
Required Specific Experience
- Experience with Python and Python Deep Learning Frameworks.
- Experience with FMCW radar and/or Depth Sensors.
- Research Areas: Computer Vision, Machine Learning, Signal Processing, Computational Sensing
- Host: Petros Boufounos
- Apply Now
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CV0064: Internship - Robust Estimation for Computer Vision
MERL is looking for a self-motivated graduate student to work on robust estimation in Computer Vision. Based on the candidate’s interests, the intern can work on a variety of topics such as (but not limited to) camera pose estimation, 3D registration, camera calibration, pose-graph optimization, and transformation averaging. The ideal candidate would be a PhD student with a strong background in 3D computer vision, RANSAC, and graduated non-convexity algorithms, and good programming skills in C/C++ and/or Python. The candidate must have published at least one paper in a top-tier computer vision, machine learning, or robotics venue, such as CVPR, ECCV, ICCV, NeurIPS, ICRA, or IROS. The intern will collaborate with MERL researchers to derive and implement new algorithms for V-SLAM, conduct experiments, and report findings. A submission to a top-tier conference is expected. The duration of the internship and start date are flexible.
Required Specific Experience
- Experience with 3D computer vision, RANSAC, or graduated non-convexity algorithms for computer vision.
- Research Areas: Computer Vision, Computational Sensing, Robotics
- Host: Pedro Miraldo
- Apply Now