Collaborates with a multidisciplinary team in the development of fundamental building science, building-to-grid integration, and urban science. Innovates in the development and deployment of physics-based and data analytic tools and technologies for building systems, whole buildings, energy systems, and urban systems. Communicates impactful research outcomes through internal reports, peer-reviewed publications, and conference presentations.
The candidate is expected to have a strong engineering background with demonstrated experience in building science and building energy modeling, especially in uncertainty quantification.
- PhD in architecture, architectural engineering, or mechanical engineering. Research experience in building science and building energy modeling.
- Experience with building energy modeling using EnergyPlus and/or OpenStudio
- Knowledge of conventional and emerging building modeling tools and techniques.
- Knowledge of fundamental building sciences.
- Experience with building energy modeling tools other than EnergyPlus/OpenStudio
- Experience with uncertainty quantification of system models
- Experience in working with multidisciplinary research teams.
- Experience with modern scientific computing methods for large scale simulations on high performance computing infrastructure for building energy modeling and uncertainty quantification.
- Experience in establishing research collaborations
- Excellent oral and written communication skills.
- Ability to think strategically and work independently.
- Experience supervising research work
- Experience with developing machine learning and data-analytic tools.
- Knowledge of relevant DOE Office funding mechanisms and strategic outlook
- A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.