CV0209: Internship - Visual-LiDAR/point clouds fused object detection and recognition

Mitsubishi•Cambridge, MA

53d•$6,000 - $8,000

About The Position

MERL is looking for a self-motivated intern to work on visual-LiDAR/point cloud fused object detection and recognition using computer vision. The relevant topics in the scope include (but not limited to): open-vocabulary or auto-vocabulary visual-LiDAR/point cloud object detection and recognition, domain adaptation or generalization in visual-LiDAR/point cloud object detection, data-efficient methods for visual-LiDAR/point cloud object detection, small object detection with visual-LiDAR/point cloud input, etc. The candidates with experiences of object recognition in LiDAR/point cloud are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.

Requirements

Proficiency in Python programming
familiarity in at least one deep learning framework
Experience with Python, PyTorch, and datasets with both images and LiDAR/point clouds (e.g. the nuScenes dataset, the ScanNet dataset, etc.).

Nice To Haves

experiences of object recognition in LiDAR/point cloud
PhD student with a strong background in computer vision and machine learning
published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI