Research Scientist Intern, Dynamic Radiance Fields (PhD) - Meta

Our Maps Research team explores, develops, and delivers novel and cutting-edge technologies for photorealistic 3D reconstruction of both static and dynamic environments, serving as the foundation for future AR/VR products. Our team is addressing a variety of technical challenges in the areas of dynamic 3D scene modeling and understanding, and related domains. We're looking for candidates who share a passion for exploring and solving complex, challenging problems at the intersection of CV/ML and dynamic 3D reconstruction.We are looking for an exceptional PhD student with a proven track record in developing algorithms for solving computer vision and graphics problems (e.g., neural rendering, view synthesis, dynamic reconstruction), as well as an outstanding software engineer who can prototype invented algorithms.Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

RESPONSIBILITIES

Research Scientist Intern, Dynamic Radiance Fields (PhD) Responsibilities:

• Actively drive and participate in cutting edge research for neural 3D modeling
• Develop efficient neural network models for dynamic, photorealistic 3D scenes
• Contribute research that can be applied to Meta VR/AR product development
• Together with our team, working towards a top-tier, scientific publication

MINIMUM QUALIFICATIONS

Minimum Qualifications:

• Hands-on knowledge with Neural 3D representations (Neural radiance fields, implicit, explicit & hybrid representations, SDFs, 3D Gaussian Splats) for object-, room-, or city-block sized scenes, neural rendering, etc.
• 2+ years experience with PyTorch and Python
• Currently has, or is in the process of obtaining a PhD in the field of computer vision, computer graphics, machine learning, or a related field
• Experience with neural rendering, novel view synthesis, and 3D reconstruction
• Excellent interpersonal and communication skills, cross-group and cross-culture collaboration
• High levels of creativity and quick problem solving capabilities
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

PREFERRED QUALIFICATIONS

Preferred Qualifications:

• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading conferences (e.g., SIGGRAPH, CVPR, ECCV, ICCV, NeurIPS, ICLR, ICML) or journals (e.g., PAMI, IJCV, JMLR, ToG)
• Experience with 3D representations (NeRFs, 3D Gaussian Splatting), generative models (e.g. diffusion models, GANs), and inverse rendering techniques
• Experience in 3D computer vision, graphics, and deep learning models for this domain.
• Demonstrated software engineer experience via previous internships, work experiences, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)