United States Department of Energy (DOE)

V. Yurkiv ( Aerospace and Mechanical Engineering)

The DOE SC program in Basic Energy Sciences (BES) hereby announces its interest in receiving new applications for Energy Innovation Hub projects pursuing multi-investigator, crossdisciplinary fundamental research to address emerging new directions as well as long-standing challenges for the next generation of rechargeable batteries and related electrochemical energy storage technologies. Electrochemical energy storage is typically viewed as the bidirectional interconversion of electricity and chemical potential energy using electrochemistry for the purpose of storing electrical energy for later use, with lithium (Li)-ion and lead acid batteries being representative of the current generation of electrochemical energy storage. Discovery and scientific exploration of new battery chemistries, materials, and architectures for energy storage are encouraged. Research on electrolyzer/fuel cell combinations using hydrogen or hydrocarbons as the chemical storage media are supported elsewhere within DOE programs and are specifically excluded from this FOA. Regardless of materials and electrochemical processes involved, the focus must be on fundamental scientific concepts and understanding for the next generation of batteries and electrochemical energy storage. 

1. A.  Jalilzadeh (Systems and Industrial Engineering)
2. M. Chertkov (Applied Mathematics)
3. S. Missoum ( Aerospace and Mechanical Engineering)

4. D. Moore (Natural Resources & the Environment)

UA may submit four pre-applications as the lead institution in a single- or multi-institutional team. No more than two pre-applications for each PI at the applicant institution are allowed. 

The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in research applications to explore potentially high-impact approaches in the development and use of scientific machine learning (SciML) and artificial intelligence (AI) in the predictive modeling, simulation and analysis of complex systems and processes.

High-performance computational models, simulations, algorithms, data from experiments and observations, and automation are being used to accelerate scientific discovery and innovation. Recent workshops, report, and strategic plans across the DOE have highlighted the research, development, and use of artificial intelligence and machine learning for science, energy, and security. Relevant domains include materials, environmental, and life sciences; high-energy, nuclear, and plasma physics; and the DOE Energy Earthshots Initiative, for examples. A 2018 Basic Research Needs workshop and report on scientific machine learning (SciML) and AI1 identified six Priority Research Directions (PRDs) for the development of the broad foundations and research capabilities needed to address such DOE mission priorities. The first three PRDs for foundational research are a set of themes common to all SciML approaches and correspond to the need for domain-awareness, interpretability, and robustness and scalability, respectively. Of the other three PRDs for capability research, PRD #5 (Machine Learning-Enhanced Modeling and Simulation) and uncertainty quantification are the subject of this FOA. 

No applicants // Limit: 3 // Tickets Available: 3

UA  is limited to no more than three pre-applications, or applications with one for each PI at the applicant institution.

The DOE SC program in Fusion Energy Sciences (FES) hereby announces its interest in applications in the areas of Machine Learning (ML), Artificial Intelligence (AI), and Data Resources for fusion energy and plasma sciences. The goal of this FOA is to support multidisciplinary teams aiming to apply advanced and autonomous algorithms to address high-priority research opportunities across the FES program. Applicants are encouraged to propose research in new systems for managing, formatting, curating, and accessing experimental and simulation data, provided in publicly available databases. Of high programmatic importance are approaches that support the realization of a fusion pilot plant on a decadal timescale.

No applicants // Limit: 2 // Tickets Available: 2 

AU is limited to both: • No more than two pre-applications or applications as the lead institution. • No more than one pre-application or application for each PI.

The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in receiving applications focusing on basic research in computer science that explores innovative approaches to creating distributed resilient systems for science. Such systems might be national or global in scale, linking geographically-distributed computing systems and scientific instruments, and might involve a large number of edge devices or sensors, but regardless, must manage computation and data in scalable and fault-tolerant manner. Important research challenges involve techniques for advanced middleware and operating and runtime systems, with this FOA targeting two research areas: 1) scalable system modeling, and 2) adaptive management and partitioning of resources. Advances in these areas will contribute to scaling-up our increasingly complex and interconnected scientific enterprise.

No applicants // Limit: 3 // Tickets Available: 3

UA may submit three LOIs.
Applications that are submitted by applicants that have not submitted a required LOI or pre-application may be declined without further review.

Reaching a New Energy Sciences Workforce (RENEW) aims to build foundations for Office of Science (SC) research and training at institutions historically underrepresented in the SC research portfolio. RENEW leverages SC’s unique national laboratories, user facilities, and other research infrastructures to provide undergraduate and graduate training opportunities for students and academic institutions not currently well represented in the U.S. science and technology (S&T) ecosystem. The hands-on experiences gained through RENEW will open new career avenues for participants, forming a nucleus for a future pool of talented young scientists, engineers, and technicians with the critical skills and expertise needed for the full breadth of SC research activities. Principal Investigators (PIs), key personnel, and students and postdoctoral researchers supported by RENEW awards will be invited to participate in HEP researcher meetings and/or SC-wide professional development and collaborator events.

The DOE SC High Energy Physics (HEP) program hereby announces its interest in receiving applications for the Reaching a New Energy sciences Workforce for High Energy Physics (RENEW-HEP) initiative. This program is intended to support training and research experiences in support of particle physics for members of underserved communities, with the dual goals of : (1) increasing the likelihood that participants from underrepresented populations, such as those present at minority serving institutions (MSIs)1 , will pursue a career in a Science, Technology, MSIs are understood broadly to include, but not be limited to, Historically Black Colleges and Universities (HBCUs), Primarily Black Institutions (PBIs), Hispanic Serving Institutions (HSIs), Tribally Controlled Colleges 2 Engineering or Math (STEM) related field; and (2) supporting investigators and building research infrastructure at institutions that have not traditionally been part of the particle physics portfolio.

Ticket #1: B. Parent
Ticket #2: Open

UArizona may submit two pre-applications.

Contact RDS to apply

The DOE SC program in Fusion Energy Sciences (FES) hereby announces its interest in receiving new or renewal single-investigator or small-group research applications to carry out frontier-level research in basic plasma science and engineering. The FES Discovery Plasma Science: Plasma Science and Technology–General Plasma Science (GPS) program supports research at the frontiers of basic and low temperature plasma science, including dynamical processes in laboratory, space, and astrophysical plasmas, such as magnetic reconnection, dynamo, shocks, turbulence cascade, structures, waves, flows and their interactions; behavior of dusty plasmas, non-neutral, single-component matter or antimatter plasmas, and ultra-cold neutral plasmas; plasma chemistry and processes in low temperature plasma, interfacial plasma, synthesis of nanomaterials, and interaction of plasma with surfaces, materials or biomaterials. In addition, this portfolio supports microelectronics and Quantum Information Science (QIS) research opportunities.

No applicants // Limit: 1 // Tickets Available: 1 

An entity may only submit one Concept Paper and one Full Application for each topic area of this FOA. UofA is only eligible for Topic Area 2: Smart Grid Grants

The BIL is a once-in-a-generation investment in infrastructure, designed to modernize and upgrade American infrastructure to enhance U.S. competitiveness, driving the creation of good-paying union jobs, tackling the climate crisis, and ensuring stronger access to economic, environmental, and other benefits for disadvantaged communities (DACs). 

This FOA seeks applications to address these three goals:
1. Transform community, regional, interregional, and national resilience, including in consideration of future shifts in generation and load
2. Catalyze and leverage private sector and non-federal public capital for impactful technology and infrastructure deployment
3. Advance community benefits

Topic Area 2: Smart Grid Grants (40107)
Objectives Topic Area 2 seeks to deploy and catalyze technology solutions that increase the flexibility, efficiency, reliability, and resilience of the electric power system, with particular focus on enhancing the system’s capabilities to meet the following objectives:

• increase the capacity of transmission facilities or the capability of the transmission system to reliably transfer increased amounts of electric energy; 
• prevent faults that may lead to wildfires or other system disturbances;
• integrate variable renewable energy resources at the transmission and distribution levels; and, 
• facilitate the aggregation and integration (edge-computing) of electric vehicles and other grid-edge devices or electrified loads.

Ticket #1: K. Johns

UArizona may submit three applications.

The DOE SC High Energy Physics (HEP) program hereby announces its interest in receiving applications for the REaching a New Energy sciences Workforce for High Energy Physics (RENEW-HEP) initiative. This program is intended to support training and research experiences in support of particle physics for members of underserved communities, with the dual goals of : (1) increasing the likelihood that participants from underrepresented populations, such as those present at minority-serving institutions (MSIs) , will pursue a career in a Science, Technology, Engineering or Math (STEM) related field; and (2) supporting investigators and building research infrastructure at institutions that have not traditionally been part of the particle physics portfolio.

No applicants // Limit: 2 // Tickets Available: 2 

Applicant institutions are limited to both:

• No more than two pre-applications or applications as the lead institution in a multiinstitution team; and
• No more than one pre-application or application for each PI.

UArizona may submit four Letters of Intent.

Ticket #1: T. Eifler

The DOE SC program in High Energy Physics (HEP) hereby announces its interest in new applications for support of Artificial Intelligence Research for High Energy Physics. This FOA refers to research into and development of computational systems that take action to achieve a goal as Artificial Intelligence (AI) research and may include Machine Learning (ML) techniques as appropriate. This Announcement invites applications for AI research in three topic areas: AI research for HEP, HEP for AI research, and development of an HEP AI Ecosystem. The HEP program explicitly encourages applications from non-traditional HEP institutions and researchers that may broaden the participation in HEP research and the AI sub-field.