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Solicitation LAB-24-3305 is referenced on the page but no deadline is visible in the page content; full details are in the PDF solicitation document.
Hardware-Aware Artificial Intelligence for High Energy Physics is sponsored by U.S. Department of Energy (DOE) Office of Science, Office of High Energy Physics (HEP). This initiative funds research that aims to transform the capabilities and efficiency of scientific tools and instrumentation by integrating artificial intelligence (AI) and machine learning (ML) with the hardware used to operate and control these technologies.
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HEP Artificial Intelligence (AI) | U.S. DOE Office of Science(SC) Artificial Intelligence (AI) Image courtesy of MicroBooNE Collaboration Image of a candidate low energy ν e interaction produced in the MicroBooNE detector by a beam of ν μ particles.
The MicroBooNE Deep Learning Low Energy Excess analysis used semantic segmentation to identify the detector hits as being caused by protons (HIP), muons (MIP), or electrons (Shower) based on their unique signatures in the MicroBooNE detector. The left image shows the raw ionization collected and the right image shows the result of the DL algorithm to associate the pixel hits with a specific particle.
High Energy Physics (HEP) supports fundamental research for public benefit to understand how our universe works at its most fundamental level. A long-standing core part of achieving that mission is the development and application of Artificial Intelligence (AI), Machine Learning (ML), and computational technology to augment or automate human skill.
AI/ML have the potential to transform HEP research by harnessing DOE investments in experiments that produce massive datasets, improve operations at scientific user facilities, development of new models and algorithms that further understanding of fundamental AI/ML technologies, and make use of high-performance computing platforms.
In a January 2018 Basic Needs Workshop, six Priority Research Directions were identified: Domain-Aware Scientific Machine Learning Interpretable Scientific Machine Learning Robust Scientific Machine Learning Data-Intensive Scientific Machine Learning Machine Learning-Enhanced Modeling and Simulation Intelligent Automation and Decision Support HEP Funding Opportunity Announcements & Awards Lists Hardware-Aware AI for HEP Review Review of Applications in Hardware-Aware AI Research for High Energy Physics A webinar was held May 29th, 1-2pm ET.
( Slides | Video ) Data, Artificial Intelligence, and Machine Learning at Scientific User Facilities Team: Department of Energy Announces $6. 4 Million for Artificial Intelligence Research in High Energy Physics Seed: Department of Energy Announces $4. 3 Million for Research on Artificial Intelligence in High Energy Physics Computational HEP & AI Initiative jeremy.
love@science. doe. gov New Precise Calculation of Nuclear Beta Decays Paves the Way to Uncover Physics Beyond the Standard Model Theorists identify new effects needed to compute the nuclear beta decay rate with a precision of a few parts in ten thousand.
Belle II Detector Produces World’s Most Precise Measurements of Subatomic Particle Lifetimes Particle lifetime measurements with early data from the Belle II experiment at the SuperKEKB accelerator demonstrate the experiment’s high precision. U.S. Department of Energy SC-25/Germantown Building 1000 Independence Ave. , SW Leaving Office of Science The link you have requested will take you to a website outside the Office of Science.
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Based on current listing details, eligibility includes: National Laboratories and universities eligible per the lab call solicitation. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates $6.4 million and $4.3 million referenced as recent funding rounds Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
The current target date is rolling deadlines or periodic funding windows. Build your timeline backwards from this date to cover registrations, approvals, attachments, and final submission checks.
Federal grant success rates typically range from 10-30%, varying by agency and program. Build a strong proposal with clear objectives, measurable outcomes, and a well-justified budget to improve your chances.
Requirements vary by sponsor, but typically include a project narrative, budget justification, organizational capability statement, and key personnel CVs. Check the official notice for the complete list of required attachments.
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Review timelines vary by funder. Federal agencies typically take 3-6 months from submission to award notification. Foundation grants may be faster, often 1-3 months. Check the program's timeline in the official solicitation for specific dates.
Many federal programs offer multi-year funding or allow competitive renewals. Check the official solicitation for continuation and renewal policies. Non-competing continuation applications are common for multi-year awards.
Research on Circular Economy, Smart Manufacturing, and Energy-Efficient Microelectronics is sponsored by U.S. Department of Energy (DOE) Advanced Materials & Manufacturing Technologies Office (AMMTO). This funding opportunity supports innovative technology R&D across the manufacturing sector with a focus on circular economy, smart manufacturing, and energy-efficient microelectronics. While the stated deadline for full applications has passed, AMMTO frequently issues similar solicitations, and this highlights a relevant area of interest for the DOE.
America's Seed Fund (SBIR/STTR) - Cybersecurity and Authentication is sponsored by U.S. National Science Foundation (NSF). Supports startups and small businesses to translate research into products and services, including cybersecurity and authentication, to secure national defense and protect the public. Includes research requiring privacy and security-preserving resources for artificial intelligence.