Digital Twin & Agentic AI Specialist (Building Energy)

University of California-Berkeley; Berkeley (CA); On-site; USD 66,000 - 91,000; Full time

Job summary

University of California, Berkeley seeks an Assistant/Associate Specialist to develop agentic AI and digital twin technology for AEC-FM applications in collaboration with LLNL. Travel to LLNL is required as part of the role, with a focus on energy management across STAR buildings and campus-scale expansions.

The role entails literature reviews, platform evaluations, prototype development, and reporting on architectures and cybersecurity for workshops with LLNL and national labs.

Qualifications

• Bachelor's degree or equivalent international degree.
• Master's degree preferred in Civil Engineering or related field.
• Knowledge of Lean Construction, including Choosing by Advantages decision-making system.
• Experience or exposure to Agentic AI frameworks, ML, or autonomous software agents.
• Understanding of cybersecurity protocols related to infrastructure management systems.

Responsibilities

• State-of-the-Practice Review: literature review on digital twins with agentic AI in AEC-FM.
• Platform Evaluation: compare commercial software platforms for campus-scale digital twin compatibility.
• Methodology & Framework Design: participate in stakeholder workshops and apply CBA decision-making.
• Prototype Development: build a functional agentic-AI digital twin prototype at building scale with real-time energy data.
• Simulation & Data Modeling: design a BIM+IoT data integration framework for simulations.
• Reporting & Stakeholder Engagement: draft reports on architectures and cybersecurity for LLNL and national laboratories.

Skills

Lean Construction

Choosing by Advantages

Agentic AI

Machine Learning

Autonomous Agents

Cybersecurity

Education

Bachelor's degree

Master's degree in Civil Engineering or related field

Job description

University of California, Berkeley seeks an Assistant/Associate Specialist to develop agentic AI and digital twin technology for AEC-FM applications in collaboration with LLNL. Travel to LLNL is required as part of the role, with a focus on energy management across STAR buildings and campus-scale expansions.

The role entails literature reviews, platform evaluations, prototype development, and reporting on architectures and cybersecurity for workshops with LLNL and national labs.