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Job ID: 144449

Opportunity in Machine Learning and Date Science for Buildings
Oak Ridge Associated Universities

Date Posted Jul. 10, 2020
Title Opportunity in Machine Learning and Date Science for Buildings
University Oak Ridge Associated Universities
Washington, D.C., United States
Department Department of Energy
Application Deadline Open until filled
Position Start Date Oct. 10, 2020
  • Graduate Student
  • Electrical and/or Electronics
    Computer Engineering
    Computer Science
    Civil Engineering
    Engineering Physics

In 2016, residential and commercial buildings consumed more than 40 percent of the Nation’s total energy and more than 74 percent of the electrical energy, resulting in an estimated annual national energy bill totaling more than $380 billion. Widespread adoption of existing energy-efficiency (EE) building technologies – and the introduction and use of new technologies – could eventually reduce energy use in homes and commercial buildings by 50 percent. This would save almost $200 billion annually on energy bills and help create jobs. BTO’s mission is to support research and development (R&D), validation, and integration of affordable, energy-saving technologies, strategies, analytical tools, and information services to enable industry and others to deploy these at scale to reduce energy use and cost in both new and existing residential and commercial buildings.

In addition to EE, Buildings Technologies Office (BTO) has several additional co-emphases. An important one is demand-flexibility (DF), a building’s ability to responsively shape its electricity draw at time scales ranging from multiple hours to seconds and even sub-seconds in order to support grid stability, reliability, and the efficient use of a variety of generation resources. Grid-interactive Efficient Buildings (GEB) is a cross-cutting BTO initiative aimed at establishing building-based DF as a key grid asset. Other emerging co-emphases include resilience, embodied energy, EE and DF at the neighborhood scale, and a new focus on technologies and strategies that support the building construction and deep renovation process; the latter is led by the Advanced Building Construction (ABC) initiative.

BTO’s Emerging Technologies (ET) Program aims to enable the development of cost-effective technologies that can reduce building energy use intensity by 45 percent by 2030, relative to 2010 EE technologies. The ET Program works towards this goal by supporting early-stage R&D in component technologies for heating, ventilation, air-conditioning, and refrigeration (HVAC&R) and water heating; solid-state lighting; and windows and opaque building envelope. The ET Program also has sub-programs in integration technology areas including Sensors and Controls (S&C).

Machine learning (ML) and data science (DS) are playing increasingly large roles in many sectors and are beginning to make an impact in buildings sectors as well with applications in building control, grid response, equipment and system-level fault detection and diagnostics, measurement and verification (M&V), demand forecasting, planning, and others. BTO’s ET Program seeks a talented and committed individual to help it enhance and expand its use of machine learning and data science throughout its portfolio.

Through this appointment, you will learn how to:

  • Participate in technical reviews/assessments of proposed research and development plans, conduct technical and economic feasibility analysis, as well as evaluate at a deep technical level the progress and ongoing viability and success potential of projects toward meeting the BTO energy efficiency goals. This includes periodic technical reviews and providing rigorous technical feedback for funded R&D projects; as well as collaborating in the negotiation of statements of work and project management plans with technically rigorous milestones, go/no-go decision points, stage-gates and deliverables for new awards.
  • Collaborate with BTO staff in the assessment of the state-of-the-art scientific literature and practice in machine learning and data science in building applications and in evaluating the potential of machine learning and data science in various building applications.
  • Collaborate with BTO staff in developing short, medium, and long term strategies and goals for the use of machine learning and data science in building applications.
  • Engage integration of BTO’s machine learning and data science activities with other efforts across EERE, DOE, and with external stakeholders.

Selected participants will receive a stipend as support for their living and other expenses during this appointment. Stipend rates are determined by EERE officials and are based on the candidate’s academic and professional background. Relocation expenses, not to exceed $5,000, incurred in relocating from the participant’s current address to Washington, D.C. (if more than 50 miles from the address shown on the application), may be reimbursed. Participants will receive a travel allowance of $10,000 per appointment year to cover travel-related expenses to scientific and professional development activities.

This opportunity is available to U.S. citizens and Lawful Permanent Residents. (LPR).

For more information about the EERE Science, Technology and Policy Program, please visit…
Program eligibility requirements can be found at: visit…

Preferred qualifications include:

  • An advanced degree in Computer Science or a related field with a focus on machine learning and data science.
  • Knowledge of energy efficiency concepts.
  • Good written and oral communication skills.

A complete application consists of:

  • An application
  • Transcript(s) – For this opportunity, an unofficial transcript or copy of the student academic records printed by the applicant or by academic advisors from internal institution systems may be submitted. Selected candidate may be required to provide proof of completion of the degree before the appointment can start.
  • A current resume/curriculum vitae (CV)
  • Two Letter of Recommendations

The resume/CV must include the following:

  • Basic applicant Information: Name, address, phone, email, and other contact information.
  • Work & Research Experience: List all work and research experiences beginning with current or most recent. Include the name of the employer, location, position held, and time period involved.
  • Leadership Experience: List experiences (e.g., work, civic, volunteer, research) that demonstrate your leadership skills. Detail your role, type of experience, organization, location, and duration.
  • Educational History: List all institutions from which you received or expect to receive a degree, beginning with current or most recent institution. Include the name of the academic institution, degree awarded or expected, date of awarded or expected degree, and academic discipline.
  • Honors & Awards: List in chronological order (most recent first) any awards or public recognitions. Include the name of awarding institution, title of the award or honor, and date of award or honor.

If you have questions, please send an email to Please list the reference code for this opportunity in the subject line of your email.

Please reference in your cover letter when
applying for or inquiring about this job announcement.

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