Remote Learning Access Florida

The Naval Research Laboratory (NRL), the corporate research laboratory for the U.S. Navy and Marine Corps, solicits white papers for basic and applied scientific research under BAA N00173-24-S-BA01. AI-related focus areas include data management and exploitation technologies that apply emerging mathematics and machine learning techniques, multi-agent systems and reinforcement learning, and AI/ML integrated with systems engineering across radar, antennas, and information systems. Additional priority areas include advanced materials and energy storage, spacecraft systems and propulsion, cybersecurity and cryptographic technologies, ocean acoustics and remote sensing, and virtual/mixed reality systems for training and situational awareness. NRL is distinct from the Office of Naval Research (ONR) and funds research conducted at or in collaboration with NRL's own laboratories. White papers are evaluated on a rolling basis with selected proposers invited to submit full proposals.

USDA NIFA's SBIR/STTR Phase I program is the principal non-dilutive federal funding channel for small businesses commercializing agricultural technology, and it has become a significant AI funding source without ever being branded as one. The program is organized into ten topic areas rather than a single AI call, which means applicants must locate their AI work inside an agricultural problem area - plant production and protection, animal production and protection, forests and related resources, food science and nutrition, rural and community development, aquaculture, biofuels and biobased products, small and mid-size farms, and agriculturally related manufacturing and alternative and renewable energy. In practice this structure rewards proposals framed around a specific production constraint rather than around a modeling technique. Machine learning for crop and soil monitoring from remote sensing, computer vision for pathogen and pest detection, autonomous harvesting and weeding robotics, decision-support and yield-prediction systems, and sensor fusion for livestock health all fit comfortably, but reviewers are agricultural scientists and commercialization specialists, not ML researchers, and proposals that lead with architecture rather than with the farm-level problem tend to score poorly. Phase I awards run from $125,000 to $181,500 for feasibility work, a range that has drifted upward in recent cycles, with Phase II available to successful Phase I awardees for full R&D. Eligibility is the standard SBIR profile: a for-profit small business concern qualifying for research or R&D purposes, US-based and majority US-owned; the STTR variant requires formal cooperative R&D with a nonprofit research institution, which is the natural route for teams spinning technology out of a land-grant university. Timing is the practical difficulty - NIFA has historically released the Phase I RFA around July with applications due roughly twelve weeks later in early October, but exact dates shift year to year and the FY2026 date was still pending at the time of review. Applicants should register on SAM.gov and Grants.gov well in advance and subscribe to Grants.gov alerts, since the release-to-deadline window is short for a program that expects a commercialization plan alongside the technical narrative.

Foresight's AI for Science and Safety Nodes RFP is a small-cheque, high-variance programme aimed at work that conventional funders will not touch, and it is one of the few AI safety calls verifiably open with a future deadline of 31 October 2026. The framing joins two problems: ensuring a safe transition to highly capable AI, and using AI to unlock scientific breakthroughs. Proposals sit in one of three layers. Local compute covers community-owned and controlled computational infrastructure - an explicitly decentralist counterweight to frontier-lab concentration. Coordination and accountability covers human-AI interaction, supercollaboration and AI risk assessment mechanisms. AI-first science covers automated nanotechnology, whole-brain emulation and frontier biotechnology. The single most important screening criterion is stated plainly: Foresight wants projects where AI is the primary engine of progress, not projects where AI is, in its words, tapped onto an existing research program - so an established lab adding a machine learning component to ongoing work is the archetype of what gets rejected. Evaluation weights alignment with a focus area, impact on AI existential risk reduction, feasibility within short AGI timelines, execution capability, and high-risk high-reward potential. Two conditions are non-negotiable: all outputs must be open-sourced, and applicants must commit to active participation at Foresight's San Francisco or Berlin nodes, which makes this poorly suited to a purely remote team. Application is a single Airtable form with a roughly three-month review.