Hands-on experience in the development and training of generative models, including diffusion, flow matching, or masked-Transformer models. Ideally, this includes either training robotic imitation policies, training models to generate whole-body motions for human avatars, or training a vision-conditioned generative model to predict actions.

Particularly:

• PhD or master's degree in generative modeling, human motion generation, or learning-based robot trajectory generation with relevant project experience.

• Strong research profile with a high track record of publications at top computer vision, graphics, and/or robotic conferences, such as ICCV/ECCV, CVPR, SIGGRAPH, CORL, RSS, etc.

• Excellent knowledge of Python and PyTorch, with hands-on experience in training and fine-tuning generative models, including diffusion, flow matching, and masked-transformer models.

• Experience in deploying learning-based trajectory generation for robotic systems.

• Experience with modern GPU-based simulations such as Omniverse or Genesis.

• Good knowledge of state-of-the-art machine learning architectures.

Additionally, the following skills are a plus but not required:

• Experience with multi-modal generative models.

• Experience with image or video-based action prediction from ego-centric views.

• Experience with ego-centric world models for human avatars or humanoid robots.

• Experience with finetuning foundation models, e.g., Gr00t or SmolVLA, to produce whole-body actions or kinematics motions.
  

We are looking for a person who enjoys working in a team in a very dynamic and fast-moving environment, and who is able and willing to take ownership of projects and decisions.