Assistant or Associate Specialist - Department of Civil and Environmental Engineering​/P2SL Re

The Department of Civil and Environmental Engineering (CEE) at UC Berkeley seeks an Assistant or Associate Specialist. The incumbent will develop agentic AI and digital twin technology with Architecture-Engineering-Construction-Facilities Management (AEC-FM) applications in collaboration with Lawrence Livermore National Laboratory (LLNL). Travel to the LLNL site is required as part of the position. Researchers in the Project Production Systems Laboratory (P2SL) use operations science and resilience engineering to deliver capital projects.

They also drive process improvements across the AEC-FM domains.

The specialist will support the LLNL infrastructure management team in evaluating, designing, and prototyping a building-scale digital twin integrated with agentic AI capabilities. Focusing initially on energy management across LLNL's STAR buildings, the specialist will document how to expand this technology to a campus-wide scale and other management applications. The specialist will own and execute critical milestones. The duties for this position include:

• State-of-the-Practice Review:
  Conduct a literature review to compile use cases of digital twins using agentic AI in the Architecture, Engineering, Construction, and Facilities Management (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 Building Information Modeling (BIM) data, site location features (coordinates, orientation, day lighting), 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.

Bachelor's degree or equivalent international degree.