Post-Doctoral Researcher: Building Data Predictive Modeling Analytics
The Fraunhofer USA Center for Manufacturing Innovation CMI in Boston, MA is part of Fraunhofer USA, a 501 (c) (3) non-profit contract R&D organization, affiliated with Fraunhofer Gesellschaft, Europe's largest contract R&D group. Fraunhofer CMI's interdisciplinary Energy Systems team is dedicated to serving the applied research needs of the sustainable energy community, helping established industry players and startup companies alike develop and deploy clean energy technologies from the laboratory to the production line. The Center has active projects in the areas of energy-efficient building technologies and integrating renewable energy with the electric grid. For additional information please view our website at CMI
About the Position:
Our team recently won an award from Department of Energy (DOE) to develop technology to enable integration of residential buildings into a smart grid. We seek a Post-Doctoral researcher who will join our team to develop, test, and evaluate algorithms using data streams from communicating thermostats and electrical sensors to automatically forecast electrical loads and their flexibility in homes. The researcher will collaborate with our industrial partner Sense and Boston University’s Center for Information and Systems Engineering CISE a team led by Prof. Paschalidis NOC. This is an exciting opportunity to delve into gigabytes of data, develop innovative ways to model and analyze the data, make a tangible contribution to the transition to a sustainable energy future, communicate results in top-tier journals and conferences, and develop relationships with industry and academia.
Responsibilities:
• Develop coarse-grained thermodynamic and machine learning techniques to model the energy performance of air conditioners, water heaters, and EV chargers, power generation of PV systems, and whole-home electricity consumption using high-resolution interval data from dozens of homes.
• Extend these models to incorporate self-calibration capabilities that accurately predict major household electrical loads and their flexibility for shifting over different time horizons.
• Integrate and automate all models into an open-source computational tool.
• Compose reports and research papers to effectively communicate research findings to different audiences.
Required Education and Experience:
• A doctoral degree in building science, engineering physics, mechanical engineering, applied mathematics, or a similar field.
• Working knowledge of heat transfer and linear differential equations.
• Experience in applying machine learning techniques (regression and classification models, cluster analysis, deep learning).
• Significant experience with programming (MATLAB, Python).
• Database experience.
• A strong interest in applying rigorous analytical techniques to “noisy” real-world problems and in learning new computational techniques.
• An excellent academic record.
• Strong verbal and written communication skills and good interpersonal skills.
In your cover letter, please explain what motivates you to apply and how, in your opinion, the skills you would develop from this Post Doc would help you achieve your next career step.
Fraunhofer USA, Inc. is an equal opportunity employer of Individuals with disabilities, protected veterans and is a federal contractor.
Apply
