Research Scientist Graduate (3D/4D Reconstruction/Generation/Relighting) - 2026 Start (PHD)

• Participate in research and development of 3D and 4D scene reconstruction/generation algorithms (including dynamic scenes and human subjects), and/or lighting estimation/editing, and promote their application in MR/XR scenarios;
• Develop efficient and scalable 3D/4D reconstruction/generation/relighting pipelines, including task formulation, dataset construction, model training and optimization, inference acceleration, and deployment;
• Collaborate closely with cross-functional teams at PICO to promote hardware–software co-innovation;
• Track the latest research progress in 3D vision and computer graphics, and contribute to papers and patents.

• Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Excellent programming skills, proficient in C++ or Python;
• Solid foundation in 3D Computer Vision and/or Computer Graphics, including:
• Multi-view reconstruction (e.g., MVS, LRM);3D/4D representations (e.g., NeRF, Gaussian Splatting);

-Familiarity with ray tracing and rasterization-based offline or real-time rendering algorithms is a plus;

• Knowledge in rendering is a plus, including fundamentals, digital asset creation and analysis (e.g. Blender, UE, Mitsuba), Image-based rendering and lighting (IBR, IBL), and lighting estimation.

• Experience or deep understanding of NeRF or 3D Gaussian Splatting (3DGS), including:
• Familiarity with 3DGS variants such as Mip-Splatting and 3DGS-MCMC;
• Understanding of dynamic Gaussian, 4DGS reconstruction, volumetric video, and feed-forward 3D/4D generation algorithms;
• Research or project experience in high-fidelity, efficient training, or compact 3D/4D representation.
• Familiarity with downstream applications of 3D/4DGS, including:- GS Relighting (e.g., GaussianShader, GIR, IntrinsicAvatar);GS Editing (e.g., Gaussian Grouping, GaussianEditor). Knowledge of inverse rendering and lighting estimation, including learning- and optimization-based models such as Differentiable Path Tracing and Physically-based Inverse Rendering;
• Familiarity with computational imaging pipelines, including sensor fusion, exposure bracketing, tone mapping, and color management for mobile camera systems
• Publications in accredited conferences or journals (CVPR, ICCV, SIGGRAPH, NeurIPS, etc.) are a plus, - - Winners or high performers in programming or algorithmic competitions such as ACM-ICPC, NOI/IOI, or TopCoder are highly preferred.