Computing Community Consortium Blog

The goal of the Computing Community Consortium (CCC) is to catalyze the computing research community to debate longer range, more audacious research challenges; to build consensus around research visions; to evolve the most promising visions toward clearly defined initiatives; and to work with the funding organizations to move challenges and visions toward funding initiatives. The purpose of this blog is to provide a more immediate, online mechanism for dissemination of visioning concepts and community discussion/debate about them.


U.S. Department of Energy Request for Information on Machine Learning for Geothermal Energy and the Geosciences

May 22nd, 2018 / in Announcements, policy, research horizons, Research News / by Helen Wright

The following is a Request for Information (RFI) from the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy. 

The Geothermal Technologies Office (GTO), within the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, announces an RFI seeking feedback from industry, academia, research laboratories, government agencies, and other stakeholders regarding research opportunities associated with applying machine learning techniques toward challenges in the geosciences that are relevant to geothermal energy.

With respect to the overall goals of establishing the practice of machine learning in the geothermal industry and maximizing the value of the rich datasets available to the geosciences, GTO is seeking input in three areas:

  • Identifying the most promising applications of machine learning in subsurface research and development (R&D);
  • Building open community datasets capable of supporting the most advanced machine learning techniques; and
  • Leveraging crowd-sourced R&D through alternative funding mechanisms.

The information requested is intended to advance GTO goals in geothermal development, though there are likely crosscutting applications with other industries operating in the subsurface. Opportunities for partnerships with other industries are also of interest.

Responses to this RFI must be submitted electronically to machinelearninggeo@ee.doe.gov no later than 5:00pm (ET) on June 6, 2018. All submissions received must include “Machine Learning RFI” in the subject of the message.

To view the RFI, please see this website.

U.S. Department of Energy Request for Information on Machine Learning for Geothermal Energy and the Geosciences

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