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SCH: Robust Multimodal Longitudinal AI for Enhanced Breast Cancer Screening is sponsored by National Cancer Institute (NCI) / NIH. This project aims to optimize and personalize breast cancer screening by advancing AI algorithms. It proposes a more realistic AI-CAD system that mimics a radiologist's approach by comparing current and prior mammograms, incorporating clinical risk factors, and accounting for image configuration variations.
It also focuses on practical implementation for clinical use, ensuring model adaptivity and computational feasibility, and will provide a comprehensive DBT database for training, evaluating, and validating models.
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Or search similar grants →According to the current listing, eligibility includes: Awardee organization is the University of Connecticut Storrs. This indicates that universities and research institutions are eligible. Confirm the full requirements in the official notice before applying.
Applications for SCH: Robust Multimodal Longitudinal AI for Enhanced Breast Cancer Screening are due July 31, 2028. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
SCH: Robust Multimodal Longitudinal AI for Enhanced Breast Cancer Screening is funded by National Cancer Institute (NCI) / NIH. Verify program details on the funder's official page before applying.
This opportunity targets applicants in Connecticut. If your organization operates elsewhere, check the official notice for location requirements.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
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This Horizon Europe Research and Innovation Action under the EU Frontier AI Initiative will fund one large consortium to develop and train a European frontier AI model with state-of-the-art multimodal performance, optimised for agentic capabilities such as tool use, reasoning and autonomous problem-solving. The project must also advance research on cutting the compute demands of frontier models (compression, distillation, efficient architectures, low-compute training and inference) and on safety topics including misalignment, reward hacking, interpretability and robustness. Work starts at TRL 2 and must reach TRL 4, with links to RAISE, AI Factories, Data Labs, ADRA and the GenAI4EU Hub. The call opens November 17, 2026 and closes March 18, 2027.
Microsoft Research's global academic research program providing Azure AI compute credits and access to cutting-edge foundation models for research advancing AI safety, human-AI interaction, and scientific discovery. The program operates through periodic Calls for Proposals, workshops, and conferences. Focus areas include AI safety and responsibility (robustness, transparency, evaluation methods), human-AI interaction (trust, creativity, productivity, reducing digital divides), and scientific discovery (knowledge discovery, hypothesis generation, multimodal data generation). The program is evolving into the Agentic AI Research and Innovation (AARI) initiative focusing on intelligent agent systems.
PAR-27-062 creates a three-year, $80,000-salary postdoctoral career award across NCI, NIAID, NIBIB, NIDCR and NINDS — and it carries a hard eligibility window that closes two years after you start your postdoc. Full analysis of the Academic Career Excellence Award, what it replaces, and why the timing rule is the whole competition.
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Read articleNIH's RFI on administrative burden (NOT-OD-26-118) takes comments through December 8, 2026. It asks seven questions, lists eight reforms already made, and warns that responses may be posted unredacted. Here is what NIH is really asking for, what it can and cannot change alone, and how researchers and research offices should respond.
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