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This Subcontract opportunity from Department Of Energy was posted on March 18, 2026. The submission period has ended. Browse the details below for market research, or find similar active opportunities.

Machine Learning/AI Interface Definition for Accelerator Operations

Closed
Federal

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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AI Contract Overview

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The Machine Learning/AI Interface Definition for Accelerator Operations is a subcontract opportunity issued by Brookhaven National Laboratory under the Department of Energy. The primary objective of this contract is to define the data interfaces between PALS and various AI/ML applications. This technical framework is intended to enable AI-driven tuning, fault prediction, and the general optimization of accelerator operations. The project is categorized under NAICS code 541715 and will be performed in Upton. Interested parties must submit their responses by the deadline of March 25, 2026, following the posting date of March 18, 2026.

General Info

NAICS

541715 - Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)

Place of Performance

Upton, NY, USA

Set-Aside

NONE

Documents

This scope was carved out of 473800.

The full solicitation package (1 document), including the RFP, is on the prime solicitation, not on this scope.

View the prime solicitation

Development of the Particle Accelerator Lattice Standard (PALS) for the NP AI-Ready Accelerator Data (NARAD)

AI Contract Breakdown

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Timeline

PhaseClosed
Posted

subcontract

Response Deadline

Deadline has passed

Submission Closed

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Organization & Contact Information

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AgencyDepartment Of Energy → Brookhaven National Labor -doe Contractor
ContactsNo contacts available
OfficeN/A
Office AddressN/A
ContactsNo contact information available

Full Description

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Define data interfaces between PALS and AI/ML applications to support AI-driven tuning, fault prediction, and optimization of accelerator operations.

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