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Fraunhofer: Post-Doctoral Fellow – Building Data Analytics


We seek a Post-Doctoral Fellow to work with Fraunhofer’s interdisciplinary Building Energy Technologies team to develop, test, and evaluate algorithms using CT and interval electric field data.



FraunhoferCompany: Fraunhofer USA
Location: Boston, MA
Web: cse.fraunhofer.org
Position: Post-Doctoral Fellow - Building Data Analytics

_Contact_:
Apply online.

About the Position:

New data sources are becoming increasingly available for buildings that have the potential to provide much greater insight into building energy performance relative to monthly utility bills. In homes, the installed base of communicating thermostats (CTs) is increasing rapidly, while around 40 percent of homes have interval electric meters. By providing time-series data for space temperature and HVAC runtime, communicating thermostats offer new insights into the thermal dynamics of homes and their heating systems. This, in turn, offers the potential to assess both building enclosure and HVAC performance and retrofit opportunities, as well as post-retrofit performance evaluation, without an on-site energy audit. Similarly, interval electric data may provide new insights into HVAC system performance and, in certain cases, enclosure performance as well.

Although conceptually reasonably straightforward, the development of effective algorithms that work for the large range of residential construction, HVAC system configurations, real-world thermostat placement, and human behaviors is a rich and challenging problem. It is a problem worth solving, as effective algorithms that are integrated into energy efficiency programs have the potential to dramatically increase the rate of home energy performance audits and, by identifying retrofit opportunities customized to individual homes, to greatly increase the uptake of insulation and HVAC system retrofits.

To realize the full potential of these data, Fraunhofer has multiple research projects to develop algorithms that use CT and electric interval data to evaluate home performance (e.g., http://energy.gov/eere/articles/energy-department-invests-14-million-increase-energy-efficiency-nation-s-homes-and).  Importantly, we are working with partners who provide access to large data sets, enabling the application of both physics-based and machine-learning approaches. We seek a Post-Doctoral Fellow to work with Fraunhofer’s interdisciplinary Building Energy Technologies team to develop, test, and evaluate algorithms using CT and interval electric field data to remotely and automatically assess home energy performance, identify home-specific retrofit opportunities, and to measure and evaluate the actual energy savings of retrofit measures. This is an exciting opportunity to work with gigabytes of data, develop innovative ways to analyze it, make a tangible contribution to national energy efficiency and global warming goals, communicate results in top-tier journals and conferences, and develop relationships with industry.

Responsibilities:

  • Develop coarse-grained thermodynamic and machine learning techniques to model building energy performance and retrofit opportunities, using interval data from hundreds of homes.
  • Using these models, develop scalable computational algorithms to characterize and validate home retrofit opportunities and potential savings.
  • Compose reports and research papers to effectively communicate research findings to different audiences.
  • Help compose proposals to develop new projects.

Required Education and Academic or Fellowship Experience:

  • A doctoral degree in physics, mechanical engineering, applied mathematics, or a similar field.
  • Experience in applying machine learning techniques (e.g., regression and classification models, cluster analysis, neural networks, ensembles, random forests, etc.).
  • Significant experience with programming (Matlab, R, Python).
  • Experience with C, C++, Java a plus.
  • Working knowledge of heat transfer and linear differential equations.
  • A strong interest in applying rigorous analytical techniques to “noisy”, real-world problems.
  • An excellent academic record.
  • Strong verbal and written communication skills.
  • Strong interpersonal skills.
  • Ability to accept a high degree of responsibility in a team-based organization, combined with ability to work independently.
  • Willingness to go the extra mile to support the team’s research projects.

In your cover letter, please explain what motivates you to apply and how, in your opinion, the skills you would develop from this Post-Doctoral Fellowship 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.

The Fraunhofer Center for Sustainable Energy Systems CSE is an applied research and development laboratory dedicated to building tomorrow’s energy future today. Fraunhofer CSE accelerates the adoption of sustainable energy technologies through scientific research and engineering innovation. Our staff’s expertise in solar photovoltaics, smart energy-efficient buildings, and grid technologies provides a platform for collaborative R&D with private companies, government entities, and academic institutions. Fraunhofer CSE is one of seven centers of Fraunhofer USA, a 501(c)(3) non-profit contract R&D organization, a subsidiary of Fraunhofer Gesellschaft, Europe's largest contract R&D organization.


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