World Model Research & Training

World model research & training At Universal Robots , part of Teradyne Inc., Our vision is to create a world where people work with robots, not like robots. And as the market leader with 75,000+ collaborative robots (cobots) already installed worldwide, we’re well on the way to achieving it. We employ 1000+ people in offices across North America, South America, Europe and Asia and we’re growing all the time. Our team is made up smart, creative people working at the forefront of automation. Together we find innovative solutions to some of the most important manufacturing issues facing businesses today. We dare to do what others find impossible- working with advanced technologies to change in the way businesses operate, so if you’re looking to build your career with a ground-breaking technology company in dynamic environment with career advancement UR is the place for you. Our Purpose At Teradyne Robotics, including Universal Robots (UR) and Mobile Industrial Robots (MiR), our mission is simple:Automation for Anyone. Anywhere. Our vision is to create a world where people work with robots, not like robots. With more than 100,000 collaborative robots deployed worldwide, Universal Robots leads the global market in collaborative automation while MiR provides cutting-edge autonomous mobile robots that transform internal logistics. Together we enable manufacturers of all sizes to automate, improve productivity, and empower people through robotics. Opportunity Overview •             Design, train, and iterate on world models that accurately capture real-world physical dynamics, robot-environment interactions, and long-horizon scene evolution •             Develop scalable training pipelines for world models using large-scale real and synthetic datasets, including data collected from deployed robot fleets •             Research and implement novel architectures — including diffusion-based, autoregressive, and latent space models — to improve world model fidelity, generalisability, and inference efficiency •             Investigate and improve model conditioning strategies that allow world models to generalise across diverse environments, robot embodiments, and task types •             Drive experiments to push the frontier of predictive world modelling, including multi-step rollouts, uncertainty estimation, and sim-to-real transfer In this role, you will: 3D scene generation •             Research and develop methods for generating high-fidelity 3D scenes — including geometry, texture, lighting, and object placement — for use in robotics simulation environments •             Explore and implement neural scene representations such as NeRF, Gaussian splatting, and implicit surface models for fast, photorealistic simulation asset generation •             Build pipelines that automatically generate diverse, physically plausible simulation environments from minimal inputs such as floor plans, semantic maps, or real-world…