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"NERSC ERCAP Energy Research Computing Allocations on Perlmutter for AI and ML" is currently closed and not accepting applications.
ERCAP (Energy Research Computing Allocations Process) allocates compute time on NERSC's Perlmutter supercomputer at Lawrence Berkeley National Laboratory, one of the world's largest GPU-based systems with over 7,000 NVIDIA A100 GPUs alongside AMD EPYC CPUs.
ERCAP is the primary mechanism for the broader DOE Office of Science research community (and partner researchers) to access leadership-class GPU resources for AI/ML, foundation model training, scientific AI, climate AI, materials AI, and AI-coupled simulation. Annual allocation cycle: submissions typically open in August and close in early October, with allocations starting January 1 of the following year.
The 2026-cycle ERCAP call closed October 8, 2026 for 2027 allocations. NERSC supports both pre-trained foundation model fine-tuning and pre-training campaigns, with a growing AI/ML user community. Recipients receive both compute hours and consultation from NERSC's NESAP for AI program providing hands-on optimization, MLOps support, and access to advanced features (interactive nodes, Jupyter at scale, ML-optimized storage).
ERCAP complements DOE's INCITE (largest) and ALCC (mission-aligned) allocation pathways.
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Or search similar grants →According to the current listing, eligibility includes: Researchers at U.S. universities, national laboratories, and DOE-affiliated industry whose research is aligned with DOE Office of Science mission areas (energy, materials, biological/environmental, fusion, high-energy physics, nuclear physics, advanced scientific computing). Foreign collaborators eligible through U.S. PIs. Industry researchers eligible for non-proprietary research with broad scientific impact. PIs must have established track record in scientific computing or AI/ML applied to scientific problems. Confirm the full requirements in the official notice before applying.
The current listing shows annual ERCAP allocations on NERSC's Perlmutter supercomputer (7,000+ NVIDIA A100 GPUs + AMD CPUs). Typical AI/ML allocations range from 1,000 to 100,000 GPU node-hours, valued at $50,000 to $5 million USD in compute resources per allocation. Multi-million-hour allocations available for very large AI training campaigns. Verify award ceilings, matching requirements, and allowable costs in the official notice.
The published deadline was October 8, 2026, which has passed. Check the official notice for any future application windows before investing time in a proposal.
NERSC ERCAP Energy Research Computing Allocations on Perlmutter for AI and ML is funded by U.S. Department of Energy Office of Science (NERSC at Lawrence Berkeley National Laboratory). Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
The ONR Long Range Broad Agency Announcement (N00014-25-S-B001) is the Office of Naval Research's primary mechanism for soliciting research proposals across all naval science and technology priority areas. The BAA accepts proposals on a rolling basis through September 30, 2026 and covers ONR's full spectrum of research interests with particular emphasis on AI-related topics including autonomous maritime systems, human-machine teaming, machine learning for sensor fusion, cooperative autonomous swarm technology, undersea autonomy, and AI-enabled decision superiority. Proposals can be funded through multiple mechanisms including individual investigator grants, the Young Investigator Program (~$510K over 3 years for early-career faculty), and Multidisciplinary University Research Initiative (MURI) awards ($1.5M/year for 3-5 years for research teams). ONR recommends contacting relevant program officers before submitting to discuss alignment with current research priorities. The BAA supports basic research (6.1), applied research (6.2), and advanced technology development (6.3) across the full range of naval-relevant science and engineering disciplines.
PRIMED-AI is a new NIH Common Fund programme, Precision Medicine with AI: Integrating Imaging with Multimodal Data, which combines medical imaging with other health data to build AI-powered clinical decision support tools for personalised medicine. This opportunity, RFA-RM-27-014, funds the programme's Validation Center: a dedicated hub that independently evaluates and characterises the AI-enabled, image-based multimodal clinical decision support tools developed elsewhere in the consortium. The structural logic matters for applicants. NIH has separated tool-building from tool-validation and is paying separately for the second, which signals institutional recognition that self-reported performance claims from developing teams are not sufficient evidence for clinical AI. The Center's remit covers verification, validation, interoperability and uncertainty quantification, so the deliverable is a reproducible evaluation capability rather than a set of papers. It operates as a U54 cooperative agreement, meaning NIH staff are substantively involved and the Center must work in concert with the PRIMED-AI Logistics Center and the Data-to-Model and Model-to-Clinic award recipients. The RFA was released on 30 June 2026 with applications due 2 October 2026. For academic groups that have built credible AI evaluation methodology, particularly in radiology, imaging informatics or biostatistics, this is an unusually direct route to becoming the reference evaluator for a major federal AI health programme, and the position carries influence over how clinical AI performance gets measured well beyond the life of the award.
RFA-RM-27-015 funds a single Logistics Center as the coordinating hub of the NIH Common Fund's PRIMED-AI program, Precision Medicine with AI: Integrating Imaging with Multimodal Data. The award is a cooperative agreement, meaning substantial NIH scientific and programmatic involvement is built in rather than incidental, and it is administered by NIBIB on behalf of the Common Fund. The Center operates through three integrated cores: Administration, which runs the steering committees and develops consortium policies; Evaluation, which assesses progress across the funded portfolio; and Outreach, which maintains a web portal for sharing AI-based clinical decision support tools and facilitates engagement between researchers, clinicians and patient communities. The strategic point for applicants is that this is the only coordinating award in a multi-award program whose other components, the Data-to-Model Academic-Industrial Partnerships, Model-to-Clinic and Multi-use Frameworks Playbook RFAs, are separately competed and separately funded. The Logistics Center therefore does not generate its own AI models; it is scored on its capacity to run a consortium, set data and tool-sharing policy, and build infrastructure that makes other teams' clinical decision support tools discoverable and reusable. Applications are due by 5:00 PM local time of the applicant organization on the date specified for the relevant review cycle, with an earliest project start date of 2 October 2026 and a maximum project period of five years.
DE-FOA-0003665, the Office of Science Open Call, accepts applications any day until September 30, 2027. But six program offices run their own comparative review panels on their own dates, and a proposal that arrives after a panel's acceptance date waits a year. Here is how to read the hidden calendar.
Read articleThe Trump administration committed $5B and 278 projects across 15+ agencies to the Genesis Mission. Here's the structure — and how to position for the next wave.
Read articleOn July 22, 2026, the White House announced more than $5 billion in federal commitments for the Genesis Mission — the Department of Energy-led national initiative to fuse AI, exascale computing, and the 17 national labs into a single scientific platform. Nearly 300 projects across all 50 states were selected from a historic Request for Applications, and 15-plus agencies are now attaching funding to a set of National Science and Technology Challenges. Here is what the Genesis Mission is, how the money is structured, and the concrete on-ramps for researchers, universities, and small businesses who were not in the first cohort.
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