Sponsor LOI Deadline (highly encouraged): April 30, 2019 by 5 PM
FAS/SEAS/OSP Deadline: May 22, 2019
Sponsor Full Proposal Deadline: May 30, 2019 by 5 PM
Award Amount: Award amount and period of performance varies by research track (see below)

The DOE SC program in High Energy Physics (HEP) invites applications for new opportunities to develop the design and execution plans for small projects to carry out dark matter particle searches, making use of DOE laboratory infrastructure and/or technology capabilities. Awards made under this FOA are to support research in developing plans for small projects that support one of the following Priority Research Directions (PRDs) defined in the  Basic Research Needs (BRN) workshop on Dark Matter New Initiatives, which was held on October 15-18, 2018:
  1. Create and detect dark matter particles and associated forces below the proton mass, leveraging DOE accelerators that produce beams of energetic particles.
  2. Detect individual galactic dark matter particles below the proton mass through interactions with advanced, ultrasensitive detectors.
  3. Detect wave dark matter using innovative technologies with emphasis on resolving a decades old mystery of the physics inside the nucleus, the so-called "QCD axion".

The new opportunities supported in this FOA are divided into two separate "Tracks" that respond to the PRDs. Proposals must only address one track:

  • Track 1 ($500K - $2M) calls for 1 to 2 years of effort to pursue planning activities that culminate in a design report and execution plan for carrying out the small project.
  • Track 2 ($750K - $4M) calls for 2 to 4 years of effort with the first 1 to 2 years used to pursue near-term technology research and development followed by 1 to 2 years to pursue design studies that culminates in a design report and execution plan for carrying out the small project. The near-term technology studies can include technology development, demonstration and risk reduction efforts.

Applications may be submitted by either a single institution or a multi-institutional consortium. Funds are intended primarily for costs for engineering and technical efforts, apparatus, materials and supplies, management and travel related to the planned efforts. Limited university scientific staff may be supported. Funds are not intended to carry out the fabrication or operations phases of the small project. HEP expects that a process to be announced in the future will provide a mechanism for small project designs resulting from this FOA to apply and be considered for funding to move in to the small project fabrication phase.

Sponsor Deadline for Letters of Intent (required):  May 8, 2019
FAS/SEAS/OSP Deadline:  May 23, 2019
Sponsor Deadline for Full Proposals:  May 31, 2019
Award Information:  $150,000 per year for 2 years. Approximately 2-6 awards are expected. 
In support of the Executive Order on Maintaining American Leadership in Artificial Intelligence, the DOE Artificial Intelligence (AI) Program and DOE Office of Science (SC) program in Advanced Scientific Computing Research (ASCR) hereby announce their interest in the co-design of learning systems and AI environments that significantly advance the field of AI for public benefit within DOE's Congressionally-authorized mission-space. The principal focus of this FOA is on Uncertainty Quantification (UQ) for AI validation and prediction. Foundational research is needed for strengthening the mathematical and statistical basis of validating machine learning and AI predictions from data generated by the Office of Science's user facilities and scientific simulations. A critical open question for scientific machine learning (SciML) is: How do we make reliable predictions and uncertainty estimates from machine learning and AI models? Predictions can be greatly improved by including input uncertainties and insights from model discrepancies. Research advances will be needed in methods that incorporate mathematical, statistical, scientific, and engineering principles for uncertainty estimates in extrapolative predictions. Furthermore, extensive literature in statistics can be leveraged for improving the model validation process. Advances in UQ will greatly enhance the mathematical and scientific computing foundations for accelerated research insights from SciML and AI.
DOE will not consider funding multi-institution collaborations under this FOA. An individual may participate in no more than two applications. If the same individual is a project member on more than two applications, the most recently received applications that match a qualified Letter of Interest (LOI) will be accepted and all other applications may be declined without merit review.

Questions about these announcement may be directed to Jennifer Corby (jcorby@fas.harvard.edu, 617-495-1590) or Susan Gomes (sgomes@fas.harvard.edu, 617-496-9448).
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