AI Research Intern – Predictive World Model

Drive a focused research project on predictive world models, spanning problem formulation, architecture design, training, evaluation, and empirical analysis…

Extend prediction beyond 2D pixels into a shared multimodal latent space that spans 3D scene representations such as Gaussian Splatting…

Investigate cross-embodiment generalization through unified observation and action representations and embodiment-conditioning mechanisms, so that a single world model transfers across vehicles, robots…

Build evaluation methodology for predictive world models, spanning representation quality, prediction accuracy, generation fidelity, physical plausibility, long-horizon rollout consistency, and closed-loop policy performance.

Currently pursuing a PhD in Engineering, Computer Science, or a related field, with a focus on Deep Learning, Computer Vision, or Generative Models.

First-author publications at top-tier venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, CoRL, RSS…

Strong, up-to-date foundation in generative modeling and experimental methodology, with hands-on experience building, training, fine-tuning, and evaluating models in PyTorch or JAX.

Strong Python programming and software design skills, with a solid understanding of data structures, algorithms, code optimization, and large-scale data processing.