About the role
As an Researcher intern, you will own a research problem in this area, scoped to fit your expertise and the team's current direction. You'll collaborate with researchers and industry domain experts, and your work will help shape how Autodesk brings AI to AEC workflows. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Research: Define and investigate an open research question in machine learning and AI for AEC data, from problem framing through experiments and results
- Model development: Design and train AI and machine learning models, including graph-based and temporal models, on large, heterogeneous real-world project data
- Uncertainty and evaluation: Build predictive models with reliable uncertainty estimates, and evaluate them rigorously against strong baselines
- Data preparation: Prepare, integrate, and validate data from multiple sources, including knowledge graph representations
- Communication: Document methods and results, present findings, and potentially contribute to research publications
- You will work as an individual contributor within a research team
More about this role
26WD101430
Position Overview
Autodesk Research is looking for a PhD Researcher Intern in machine learning to help advance AI for the architecture, engineering, and construction (AEC) industry.
AEC projects generate large amounts of data across design, planning, and construction. Our team studies how machine learning and AI can connect this data and learn from them to understand, predict, and reason about how projects unfold.
As an Researcher intern, you will own a research problem in this area, scoped to fit your expertise and the team's current direction. You'll collaborate with researchers and industry domain experts, and your work will help shape how Autodesk brings AI to AEC workflows.
Responsibilities
- Research: Define and investigate an open research question in machine learning and AI for AEC data, from problem framing through experiments and results.
- Model development: Design and train AI and machine learning models, including graph-based and temporal models, on large, heterogeneous real-world project data.
- Uncertainty and evaluation: Build predictive models with reliable uncertainty estimates, and evaluate them rigorously against strong baselines.
- Data preparation:...
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