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Somil Bansal

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Two Postdoctoral Positions in Safe Autonomy, Robotics, Machine Learning, and Foundation Models at Stanford Stanford University in United States

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

Deadline

Nov 30, 2026

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Country

United States

University

Stanford University

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Keywords

Computer Science
Electrical Engineering
Aerospace Engineering
Uncertainty Analysis
Formal Methods
Aviation
Robotics
Control System
ML

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About this position

Stanford AeroAstro’s Safe and Intelligent Autonomy Lab (SIA Lab) is recruiting two postdoctoral researchers for projects at the intersection of robotics, machine learning, control, and safety.

The first position focuses on the safety of learning-enabled autonomous systems through a NASA University Leadership Initiative project involving academic, industry, and government collaborators. The research addresses design-time safety assurances and safety-aware learning, runtime safety monitoring and control, simulation-based testing and validation, and post-deployment learning and adaptation. Although the primary applications are in aviation, the methods can extend to robotics and other autonomous systems.

The second position explores reliable foundation models and evaluation for robot autonomy. Potential research directions include steering and controlling foundation models, developing reliable long-horizon world models, and enabling robust planning and decision-making under model uncertainty and errors. Researchers may also develop methods to evaluate robot policies, identify failure modes, and systematically stress-test autonomous systems, particularly through simulation-based approaches.

The positions are intentionally broad, giving postdoctoral researchers flexibility to shape projects around their backgrounds and interests within the SIA Lab’s research environment at Stanford University.

Funding details

Available

What's required

Candidates should be recently graduated or soon-to-graduate PhD students with a strong research background in relevant areas. For the safety position, backgrounds in robotics, control, machine learning, formal methods, or related fields are welcome; prior aerospace experience is advantageous but not required. For the reliable foundation models position, strong research experience in robot learning, foundation models, uncertainty quantification, world modeling, or related areas is especially sought.

How to apply

Review the detailed position descriptions and submit an application through the provided application link. Applications are reviewed on a rolling basis, with full consideration by November 30, 2026.

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Professors

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University Name
Country flag

United States

Research Interests

Control System

Aerospace Engineering

Aviation

Formal Methods

Electrical Engineering