Computer Vision

MERL is looking for a highly motivated intern to work on an original research project in rendering dynamic scenes from novel views. A strong background in 3D computer vision and/or computer graphics is required. Experience with the latest advances in volumetric rendering, such as neural radiance fields (NeRFs) and Gaussian Splatting (GS), is desired. The successful candidate is expected to have published at least one paper in a top-tier computer vision/graphics or machine learning venue, such as CVPR, ECCV, ICCV, SIGGRAPH, 3DV, ICML, ICLR, NeurIPS or AAAI, and possess solid programming skills in Python and popular deep learning frameworks like Pytorch. The candidate will collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The position is available for graduate students on a Ph.D. track or those that have recently graduated with a Ph.D. Duration and start date are flexible but the internship is expected to last for at least 3 months.

Required Specific Experience

• Prior publications in top computer vision/graphics and/or machine learning venues, such as CVPR, ECCV, ICCV, SIGGRAPH, 3DV, ICML, ICLR, NeurIPS or AAAI.
• Experienced in the latest novel-view synthesis approaches such as Neural Radiance Fields (NeRFs) or Gaussian Splatting (GS).
• Proficiency in coding (particularly scripting languages like Python) and familiarity with deep learning frameworks, such as PyTorch or Tensorflow.

MERL is looking for a highly motivated and qualified intern to contribute to research in human-robot interaction (HRI). The ideal candidate is a Ph.D. student with expertise in robotic manipulation, perception, deep learning, probabilistic modeling, or reinforcement learning. We have several research topics available, including assistive teleoperation, visual scene reconstruction, safety in HRI, shared autonomy, intent recognition, cooperative manipulation, and robot learning. The selected intern will work closely with MERL researchers to develop and implement novel algorithms, conduct experiments, and present research findings. We publish our research at top-tier conferences. Start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their updated CV and list of publications.

Required Specific Experience

• Experience with ROS and deep learning frameworks such as PyTorch are essential.
• Strong programming skills in Python and/or C/C++
• Experience with simulation tools, such as PyBullet, Issac Lab, or MuJoCo.
• Prior experience in human-robot interaction, perception, or robotic manipulation.

MERL is looking for a self-motivated intern to work on visual-LiDAR fused object detection and recognition using computer vision. The relevant topics in the scope include (but not limited to): open-vocabulary visual-LiDAR object detection and recognition, domain adaptation or generalization in visual-LiDAR object detection, data-efficient methods for visual-LiDAR object detection, small object detection with visual-LiDAR input, etc. The candidates with experiences of object recognition in LiDAR 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.

Required Specific Experience

• Experience with Python, PyTorch, and datasets with both images and LiDAR (e.g. the nuScenes dataset).