Building Energy Digital Twin & AI Specialist

University of California, Berkeley

Berkeley (CA)

On-site

USD 66,000 - 91,000

Full time

12 days ago

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Job summary

The University of California, Berkeley invites applications for an Assistant or Associate Specialist in the P2SL Research Group within CEE. The incumbent will develop agentic AI and digital twin technology for AEC-FM applications in collaboration with LLNL, including travel to LLNL.

The specialist will advance energy-management digital twins and document pathways to campus-wide deployment. Responsibilities include literature review, platform evaluation, framework design, prototype development,

Qualifications

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

Responsibilities

State-of-the-Practice Review: Conduct a literature review to compile use cases of digital twins using agentic AI in the AECFM sector.
Platform Evaluation: Identify, compare, and contrast commercial software platforms and off-the-shelf technologies capable of supporting a campus-scale digital twin, assessing compatibility with existing building management systems.
Methodology & Framework Design: Participate in stakeholder workshops to characterize software platforms and to select optimal application prototypes. Assist in applying the Choosing-by-Advantages (CBA) decision-making system during stakeholder workshops.
Prototype Development: Develop a functional, prototype agentic-AI digital twin at the scale of a building that integrates real-time building energy data (e.g., metered power consumption, electricity use, plug loads).
Simulation & Data Modeling: Design a framework, taking into account current and future capabilities at the LLNL, to combine BIM data, site location features (coordinates, orientation, daylighting), IoT sensor streams, and agentic AI to run and further extend digital twins and associated simulation models.
Reporting & Stakeholder Engagement: Draft deliverables, including comprehensive reports on software/hardware architectures, cybersecurity concerns, and technical summaries for workshops with LLNL and national laboratory personnel.