Collaborate on innovative manufacturing technologies that accelerate the ability to bring life-saving and life-enhancing products to market
Help companies find ways to adopt and implement new technologies and approaches, enhancing patient access to medicines and vaccines
Cultivate a world-leading biopharmaceutical workforce through novel training and education programs
We achieve our mission through an RFx process that aims to advance technical and workforce development projects:
RFP 8.1 includes global health, technology development, and workforce development topics. It is open to current NIIMBL members and to new members by the close of the full proposal deadline.
Submission deadline is September 30, 2024.
The 2025 NIIMBL eXperience Request for Applications (RFA) is a rapid response proposal call. The purpose is to conduct the NIIMBL eXperience program in different areas of the country. We intend to do this by funding NIIMBL academic or non-profit member organizations to serve as regional lead organizations that will plan, recruit students, and run a NIIMBL eXperience program in their region. Applicants to this RFA will apply to serve as a regional lead organization by proposing a program that provides exposure to the biopharmaceutical manufacturing industry through hands-on training, mentoring, tours, and engagement with “hosts” from professional associations, industry, and academic partners.
RFP 7.1 includes both technology development and workforce development topics, with a funding amount of up to $4 million. It is open to current NIIMBL members and to new members by the close of the full proposal deadline.
Project Call 7.1 closed on December 7, 2023. Submissions are now in review.
RFI 2024.1 – Universal Connectivity for In-Process Analytics in Biopharmaceutical Manufacturing – aims to gather insights and ideas regarding the development of a Universal Connectivity System for at-line/inline analytical instrumentation. This initiative looks to support NIIMBL's Big Data program in biomanufacturing.
NIIMBL invites submissions from interested parties to share information on potential solutions for a Universal Connectivity System. This system is envisioned as an OT/IT core to facilitate seamless data integration and advanced analytics within biomanufacturing processes, thus enabling data-driven decision-making and process optimization.
RFI 2024.2 – Hybrid Model Federated Learning – seeks innovative approaches that combine Federated Learning with hybrid models (combining Physics-Based and AI/ML data-driven methodologies) specifically tailored for biopharmaceutical manufacturing processes. This request aims to gather insights and concepts for achieving integration and operationalization of these models in a practical, industry-relevant context. By fostering collaboration among a wide range of stakeholders, we aim to enhance prediction accuracy of Hybrid Models for biopharmaceutical process predictions using Federated Learning and other Privacy Preserving Computing approaches. This initiative seeks to train a centralized model using community data while maintaining data privacy.
This RFI is a preliminary step to gauge interest and gather insights which may shape future studies or funding opportunities in Hybrid Model Federated Learning. This RFI is not a solicitation for funding proposals. We encourage contributions from all interested parties to become integral parts of this innovative endeavor.
NIIMBL is pleased to announce the pilot of a faculty sabbatical in industry program. The NIIMBL Faculty Fellows program provides financial support for faculty or instructional staff at universities and community colleges to pursue a sabbatical either at an industry member (Tier 1 or Tier 2) or at NIIMBL’s Headquarters (NIIMBL HQ) in Delaware to support NIIMBL-led projects.
The NIIMBL Faculty Fellows program closes December 31, 2024.
We achieve our mission through an RFx process that aims to advance technical and workforce development projects:
RFP 8.1
RFP 8.1 includes global health, technology development, and workforce development topics. It is open to current NIIMBL members and to new members by the close of the full proposal deadline.
Submission deadline is September 30, 2024.
RFA: NIIMBL eXperience 2025
The 2025 NIIMBL eXperience Request for Applications (RFA) is a rapid response proposal call. The purpose is to conduct the NIIMBL eXperience program in different areas of the country. We intend to do this by funding NIIMBL academic or non-profit member organizations to serve as regional lead organizations that will plan, recruit students, and run a NIIMBL eXperience program in their region. Applicants to this RFA will apply to serve as a regional lead organization by proposing a program that provides exposure to the biopharmaceutical manufacturing industry through hands-on training, mentoring, tours, and engagement with “hosts” from professional associations, industry, and academic partners.
RFP 7.1
RFP 7.1 includes both technology development and workforce development topics, with a funding amount of up to $4 million. It is open to current NIIMBL members and to new members by the close of the full proposal deadline.
Project Call 7.1 closed on December 7, 2023. Submissions are now in review.
RFI 2024.1: Universal Connectivity
RFI 2024.1 – Universal Connectivity for In-Process Analytics in Biopharmaceutical Manufacturing – aims to gather insights and ideas regarding the development of a Universal Connectivity System for at-line/inline analytical instrumentation. This initiative looks to support NIIMBL's Big Data program in biomanufacturing.
NIIMBL invites submissions from interested parties to share information on potential solutions for a Universal Connectivity System. This system is envisioned as an OT/IT core to facilitate seamless data integration and advanced analytics within biomanufacturing processes, thus enabling data-driven decision-making and process optimization.
RFI 2024.2: Hybrid Model
RFI 2024.2 – Hybrid Model Federated Learning – seeks innovative approaches that combine Federated Learning with hybrid models (combining Physics-Based and AI/ML data-driven methodologies) specifically tailored for biopharmaceutical manufacturing processes. This request aims to gather insights and concepts for achieving integration and operationalization of these models in a practical, industry-relevant context. By fostering collaboration among a wide range of stakeholders, we aim to enhance prediction accuracy of Hybrid Models for biopharmaceutical process predictions using Federated Learning and other Privacy Preserving Computing approaches. This initiative seeks to train a centralized model using community data while maintaining data privacy.
This RFI is a preliminary step to gauge interest and gather insights which may shape future studies or funding opportunities in Hybrid Model Federated Learning. This RFI is not a solicitation for funding proposals. We encourage contributions from all interested parties to become integral parts of this innovative endeavor.
RFA: Faculty Fellows
NIIMBL is pleased to announce the pilot of a faculty sabbatical in industry program. The NIIMBL Faculty Fellows program provides financial support for faculty or instructional staff at universities and community colleges to pursue a sabbatical either at an industry member (Tier 1 or Tier 2) or at NIIMBL’s Headquarters (NIIMBL HQ) in Delaware to support NIIMBL-led projects.
The NIIMBL Faculty Fellows program closes December 31, 2024.
Our membership is made up of a diverse set of stakeholders from across the biopharmaceutical manufacturing ecosystem.
Members have the opportunity to collectively revolutionize current biomanufacturing platforms, processes, and educational programs and share in the benefits of these transformative solutions.
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The NIIMBL ecosystem’s collaborative efforts to accelerate innovative manufacturing technologies help to:
Biopharmaceuticals are medicines that save, sustain, and improve lives.
We offer a variety of membership options that give you the flexibility to choose your organization’s level of engagement based on technology interests and priorities.