SpAItial
Research Scientist - World Models(m/w/x)
Full-timeOn-siteNot specified
München
New Job?Nejo!
Your personal AI career agent
SPSpAItial
Research Scientist - 3D Diffusion(m/w/x)
München
Full-timeOn-site
AI/ML
Data Science
Nejo AI Summary
Pioneering 3D diffusion models for generative AI and computer vision. PhD in CS, CV, graphics, ML, or related field required. Work on world models with physics and geometry understanding.
Requirements
- PhD in computer science, computer vision, graphics, machine learning, or related field
- Top-tier publication record at CVPR, ECCV/ICCV, NeurIPS, and SIGGRAPH
- Strong deep learning and generative modeling fundamentals
- Expertise in diffusion models and large transformer models
- Hands-on training diffusion models experience
- Experience with image and video model stacks
- Solid understanding of 3D processing concepts
- Proficiency in Python
- Proficiency in deep learning frameworks like PyTorch
- Experience in large-scale model training and optimization
- Ability to implement research ideas
- Ability to run rigorous experiments
- Ability to ship reliable ML code
Tasks
- Lead research on 3D diffusion models
- Design diffusion-based methods for 3D generation
- Build 3D diffusion models
- Train 3D diffusion models
- Optimize 3D diffusion models
- Evaluate 3D diffusion models
- Research diffusion model architectures
- Research diffusion model losses
- Research diffusion model sampling strategies
- Adapt image diffusion backbones to 3D
- Adapt video diffusion backbones to 3D
- Implement 3D point cloud representations
- Implement 3D mesh representations
- Implement 3D Gaussian Splatting
- Develop training pipelines
- Develop loss functions for geometry accuracy
- Develop loss functions for visual fidelity
- Develop loss functions for spatiotemporal consistency
- Integrate physics-aware priors into diffusion systems
- Integrate world model capabilities into diffusion systems
- Analyze model performance
- Debug model failure cases
- Iterate to improve model quality
- Iterate to improve model robustness
Education
- Doctoral / PhD
Languages
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
- Stable Diffusion
- FLUX
- WAN
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of SpAItial and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
Not a perfect match?
- SpAItial
Research Engineer(m/w/x)
Full-timeOn-siteExperiencedMünchen - NVIDIA
Research Scientist, 3D Computer Vision(m/w/x)
Full-timeOn-siteNot specifiedMünchen - SpAItial
Research Engineer - Graphics(m/w/x)
Full-timeOn-siteExperiencedMünchen - SpAItial
Machine Learning & Cloud Infra Engineer(m/w/x)
Full-timeOn-siteExperiencedMünchen
SPSpAItial
Research Scientist - 3D Diffusion(m/w/x)
München
Full-timeOn-site
AI/ML
Data Science
Nejo AI Summary
Pioneering 3D diffusion models for generative AI and computer vision. PhD in CS, CV, graphics, ML, or related field required. Work on world models with physics and geometry understanding.
Requirements
- PhD in computer science, computer vision, graphics, machine learning, or related field
- Top-tier publication record at CVPR, ECCV/ICCV, NeurIPS, and SIGGRAPH
- Strong deep learning and generative modeling fundamentals
- Expertise in diffusion models and large transformer models
- Hands-on training diffusion models experience
- Experience with image and video model stacks
- Solid understanding of 3D processing concepts
- Proficiency in Python
- Proficiency in deep learning frameworks like PyTorch
- Experience in large-scale model training and optimization
- Ability to implement research ideas
- Ability to run rigorous experiments
- Ability to ship reliable ML code
Tasks
- Lead research on 3D diffusion models
- Design diffusion-based methods for 3D generation
- Build 3D diffusion models
- Train 3D diffusion models
- Optimize 3D diffusion models
- Evaluate 3D diffusion models
- Research diffusion model architectures
- Research diffusion model losses
- Research diffusion model sampling strategies
- Adapt image diffusion backbones to 3D
- Adapt video diffusion backbones to 3D
- Implement 3D point cloud representations
- Implement 3D mesh representations
- Implement 3D Gaussian Splatting
- Develop training pipelines
- Develop loss functions for geometry accuracy
- Develop loss functions for visual fidelity
- Develop loss functions for spatiotemporal consistency
- Integrate physics-aware priors into diffusion systems
- Integrate world model capabilities into diffusion systems
- Analyze model performance
- Debug model failure cases
- Iterate to improve model quality
- Iterate to improve model robustness
Education
- Doctoral / PhD
Languages
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
- Stable Diffusion
- FLUX
- WAN
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of SpAItial and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
SpAItial
Industry
Research
Description
The company is pioneering the development of a frontier 3D foundation model, pushing the boundaries of AI, computer vision, and spatial computing.
Not a perfect match?
- SpAItial
Research Scientist - World Models(m/w/x)
Full-timeOn-siteNot specifiedMünchen - SpAItial
Research Engineer(m/w/x)
Full-timeOn-siteExperiencedMünchen - NVIDIA
Research Scientist, 3D Computer Vision(m/w/x)
Full-timeOn-siteNot specifiedMünchen - SpAItial
Research Engineer - Graphics(m/w/x)
Full-timeOn-siteExperiencedMünchen - SpAItial
Machine Learning & Cloud Infra Engineer(m/w/x)
Full-timeOn-siteExperiencedMünchen