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NSF

This project aims to serve the national interest by enhancing community college education in remote sensing technologies to address the critical skills gap in the Built Environment (BE) sector. The project will develop innovative curricula, courses, and advanced certificates that prepare students for careers utilizing remote sensing technologies, such as drone operations and advanced data processing techniques. By fostering sustainability, efficiency, and technological literacy, this initiative aligns with NSF's mission to promote the progress of science, while supporting the Advanced Technological Education (ATE) program's goal of training technicians for high-demand fields. Its broader significance includes addressing national workforce demands, enhancing student employability, and promoting interdisciplinary collaboration in BE careers. The project will focus on developing and implementing industry-aligned curricula, including stackable certificates, tailored to the specific needs of the BE sector in Southwest Ohio. Project goals include integrating remote sensing technologies into coursework, equipping students with cutting-edge skills in drone operations, LiDAR, and geospatial analysis, and fostering partnerships with local industries to align training with workforce demands. The project will assess the effectiveness of the developed curricula, evaluate student outcomes, and disseminate results through academic publications and industry collaborations. By addressing the evolving needs of the BE sector, the project will generate new knowledge and practices, contributing to the field's broader understanding of how to align education with technological advancements. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the nation's economy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

FDA - Food and Drug Administration

Project Summary The U.S. retail food regulatory workforce is navigating a period of significant transition, driven by high turnover, a narrowing pipeline of new professionals, evolving technologies, and rising expectations for consistent, high-quality training. In response, the FDA’s Office of Training, Education, and Development (OTED) has established a foundation for a national Integrated Food Safety System (IFSS) that is competency-based, performance-driven, and grounded in real-world regulatory practice. The National Environmental Health Association (NEHA) proposes to build on this foundation by delivering a modern, sustainable training system that expands the reach, quality, and long-term value of FDA’s investment. NEHA will continue to collaborate with FDA and Content Advisory Groups (CAGs) to review and update FDA-aligned courses using instructional systems design principles and SMART learning objectives. Updates will reflect emerging public health priorities, regulatory changes, and adult learning best practices to ensure content remains relevant and impactful. Annually, NEHA will deliver more than 25 FDA-aligned courses through instructor-led, virtual, and hybrid formats. Each course will be supported by full-service logistics, including instructor coordination, LMS integration, technical facilitation, and post-course evaluation, ensuring consistency and accessibility across all sessions. To enhance reach and sustainability, NEHA will recruit and onboard qualified instructors, provide mentorship, and conduct ongoing performance evaluations while also strengthening the Partnership for Regulatory Education and Training (PRET), enabling SLTT professionals to deliver FDA courses within their local jurisdictions. NEHA will also implement longitudinal retention studies to evaluate how training translates to real- world application. Insights will inform targeted follow-up modules that reinforce learning, improve performance, and strengthen the SLTT regulatory workforce. NEHA’s commitment extends beyond course delivery to include leadership development, credentialing, and systems-level impact. We support innovation through AI-driven learning pathways, just-in-time training, and retention boosters tailored to learner needs. This proposal not only strengthens FDA’s current training systems but reimagines the future of food safety education, delivering high-impact, learner-centered training that protects public health and advances a unified, performance-ready workforce.

NSF

As adults undergo challenges associated with significant life challenges, for example, a new job or a new school, peoples’ achievement, motivation, and well-being often suffer. Such outcomes can undermine workforce participation and economic opportunities. This project advances a new theory and empirically tests the beneficial impacts of community connections and skill training (e.g., financial competency) to improve achievement, well-being, and workforce participation. Understanding the psychological dimensions that strengthen interpersonal connectedness and promote behavioral health can provide pathways for developing strategies to effectively help people overcome challenges throughout the lifespan. This project creates new knowledge on the relationship between connectedness and behavioral health by testing students who have recently started at new institutions. Methods include both a longitudinal and cluster randomization design, with the measurement of both qualitative and quantitative variables. Results are poised to provide an in-depth understanding of whether and how a first year course structure that includes manipulations of connectedness across five areas: academic, professional, wellness, community engagement, and financial development improve motivation and behavioral health compared to a control group. Broader impacts include the development of rich datasets that are used for training and mentoring opportunities. Motivation and persistence in education are critical for developing a 21st century workforce for key industries like AI, advanced manufacturing, and biotechnology. The use-inspired design will produce results that have potential to transform theories that relate to the self, community, and ecology with translational possibilities for resources to support successful pipelines for workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Associated General Contractors

Construction industry workforce development and apprenticeship programs

Health and Human Services Department

In compliance with the Paperwork Reduction Act of 1995, HRSA submitted an Information Collection Request (ICR) to the Office of Management and Budget (OMB) for review and approval. Comments submitted during the first public review of this ICR will be provided to OMB. OMB will accept further comments from the public during the review and approval period. OMB may act on HRSA's ICR only after the 30-day comment period for this notice has closed.

Health and Human Services Department

In compliance with the Paperwork Reduction Act of 1995, HRSA submitted an Information Collection Request (ICR) to the Office of Management and Budget (OMB) for review and approval. Comments submitted during the first public review of this ICR will be provided to OMB. OMB will accept further comments from the public during the review and approval period. OMB may act on HRSA's ICR only after the 30-day comment period for this notice has closed.

Health and Human Services Department

In compliance with the Paperwork Reduction Act of 1995, HRSA submitted an Information Collection Request (ICR) to the Office of Management and Budget (OMB) for review and approval. Comments submitted during the first public review of this ICR will be provided to OMB. OMB will accept further comments from the public during the review and approval period. OMB may act on HRSA's ICR only after the 30-day comment period for this notice has closed.

Health and Human Services Department

In compliance with the Paperwork Reduction Act of 1995, HRSA submitted an Information Collection Request (ICR) to the Office of Management and Budget (OMB) for review and approval. Comments submitted during the first public review of this ICR will be provided to OMB. OMB will accept further comments from the public during the review and approval period. OMB may act on HRSA's ICR only after the 30-day comment period for this notice has closed.

Health and Human Services Department

In compliance with the requirement for opportunity for public comment on proposed data collection projects of the Paperwork Reduction Act of 1995, HRSA announces plans to submit an Information Collection Request (ICR), described below, to OMB. Prior to submitting the ICR to OMB, HRSA seeks comments from the public regarding the burden estimate, below, or any other aspect of the ICR.

Health and Human Services Department

In compliance with the requirement for opportunity for public comment on proposed data collection projects of the Paperwork Reduction Act of 1995, HRSA announces plans to submit an Information Collection Request (ICR), described below, to OMB. Prior to submitting the ICR to OMB, HRSA seeks comments from the public regarding the burden estimate, below, or any other aspect of the ICR.

Health and Human Services Department

In compliance with the requirement for opportunity for public comment on proposed data collection projects of the Paperwork Reduction Act of 1995, HRSA announces plans to submit an Information Collection Request (ICR), described below, to OMB. Prior to submitting the ICR to OMB, HRSA seeks comments from the public regarding the burden estimate, below, or any other aspect of the ICR.

Health and Human Services Department

In compliance with the requirement for opportunity for public comment on proposed data collection projects of the Paperwork Reduction Act of 1995, HRSA announces plans to submit an Information Collection Request (ICR), described below, to OMB. Prior to submitting the ICR to OMB, HRSA seeks comments from the public regarding the burden estimate, below, or any other aspect of the ICR.

NSF

This award is for the deployment of a mobile weather radar as part of the Purdue University “Students Of Purdue Observing Tornadic Thunderstorms for Research (SPOTTR)” summer course. Students apply what they have learned in the classroom to real-world conditions during a field experience in the U.S. Great Plains to observe severe weather. The mobile radar significantly enhances this training by providing hands-on experiences with advanced observational technology. The project helps build a skilled workforce that supports improved weather forecasting and hazard preparedness. The University of Oklahoma Rapid-scan X-Band Polarimetric mobile radar (RaXPol) will be deployed first at Purdue University for orientation, training, and calibration exercises to introduce students to this advanced weather radar. Data collected during the deployment will be used for research projects and culminate in student presentations and publications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Over nearly two centuries, molecular synthesis has transformed the world by creating new molecular entities that have improved quality of life around the globe. This enterprise currently requires access to highly sophisticated equipment and extensive training. Established in 2020, the NSF Molecule Maker Lab Institute (MMLI) is dedicated to developing AI tools, automated workflows, and educational resources for the next generation of molecular innovators that will accelerate molecular discovery and broaden access to the expertise and sophisticated mechanics of molecular synthesis. The second phase of the institute (MMLI 2.0) focuses on the development and application of new foundational AI methods and generative AI models for molecule discovery and synthesis, as well as the dissemination of these tools to a broad research community. Synergistically, these activities are advancing the frontiers of AI with data requirements unique to molecular synthesis. MMLI2.0 further seeks to train a next-generation workforce with a unique set of skills in AI, chemistry, and robotics. This new generation of molecule makers represents the vanguard of innovators who will enable more efficient manufacturing and discovery of molecules with important functions and broaden access to the small molecule making process, achieving a powerful impact on the U.S. research community. Functional molecules play a critical role in addressing many grand challenges facing society today. However, the process of discovering and manufacturing such molecules has remained slow, expensive, and highly specialist-dependent. To address this grand challenge, the second iteration of the Molecule Maker Lab Institute (MMLI) seeks to advance AI-driven approaches such as foundational AI agents, large language models, and generative AI methods to accelerate the discovery and synthesis of functional molecules required for societal advance. Building upon the successes of MMLI1.0, the second phase of the institute (MMLI2.0) is addressing predictive challenges in catalysis, drug design, and material discovery by integrating new AI methodologies within closed-loop experimentation platforms, emphasizing human-in-the-loop systems, and making progress towards fully autonomous molecular discovery. In parallel, the MMLI 2.0 seeks to enhance workforce development and expert training in this emerging field through their integrated MATRIX program and continues to engage industrial collaboration through their partnership program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This project establishes the AI Research Institute on Interaction for AI Assistants (ARIA). ARIA will accelerate the development of next-generation AI assistants for mental and behavioral health—a field where trust, empathy and personalization are critical and where current AI systems fall short. ARIA embraces the integrated study of human cognition and machine cognition, treating them as inherently complementary scientific endeavors essential to achieving successful interaction with AI assistants. ARIA brings researchers in computer science, neuroscience, cognitive science, philosophy, law, and education together with mental health practitioners and civil society groups to forge new opportunities for synergistic scientific inquiry that advances technology and improves human well-being. The institute will grow a future-ready workforce through interdisciplinary education pathways from K-12 through postgraduate training, helping shape a generation that understands the technical and ethical dimensions of AI. ARIA’s research activities are centered around three pillars–Grounding, Instructability, and Alignment–that are interconnected and motivated by challenges of developing effective AI assistants for mental and behavioral health. In grounding, ARIA will develop new models for efficient learning and generalization, new learning algorithms leading to rich, causal models, computational theories for navigating the inherent trade-offs between learning algorithms or model architectures, and new evaluation metrics for tracking progress toward the goal of trustworthy AI assistants. In instructability, ARIA will design new paradigms centered on establishing trust in AI, new theories and models of how humans interact with AI, new methods for describing AI’s internal processing, and new models and training procedures for integration into a computational framework for the development of AI assistants. In alignment, ARIA will advance current best practices for human-centered design, establish precise definitions for what it means to be aligned, develop computational and experimental methods to operationalize these definitions, and develop cognitively and computationally sound metrics of alignment in complex ethical and social contexts. Across all activities, ARIA promotes integration between academia and industry, and between research and continuing education, enabling a holistic approach to AI conceptualization, development, and evaluation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Farmers around the world face growing challenges from crop pests, diseases, and weeds that threaten food production and agricultural economic prosperity. These threats are becoming more severe and traditional methods of identifying and managing them often require specialized knowledge and expensive resources that many farmers cannot access. This project develops AI-based decision support tools, such as smartphone apps and a chatbot, that help farmers quickly identify agricultural problems in real-time. By taking a photo of a pest, disease, or weed, farmers receive instant identification and practical advice on how to manage the problem effectively. Technology works like having an expert crop advisor or extension agent in your pocket, making advanced pest management accessible to farmers everywhere, from small family farms to large agricultural operations. This collaborative effort between the United States and QUAD member countries ensures that the decision support tools work effectively across different crops, environmental conditions, and farming systems in the U.S. and beyond. This project addresses the critical challenge of accurate, real-time identification and management of agricultural pests, diseases, and weeds across a variety of global farming systems. The research develops an end-to-end machine learning-based pipeline with uncertainty quantification, conformal prediction, and federated learning. The artificial intelligence-based models can identify several thousand different agricultural threats, including insect pests, weeds, and crop diseases relevant to major agricultural regions. The project employs a "global-to-local" approach that trains comprehensive Artificial Intelligence (AI) models using multi-modal international pest datasets, then fine-tunes these models for specific regional conditions and pest pressures in the United States and QUAD member countries. The technical framework combines advanced computer vision, machine learning, adaptive algorithms, and natural language processing to create smartphone applications that provide real-time pest identification and integrated pest management recommendations using a chatbot for a user-friendly interface for making queries. The system includes offline functionality for areas with limited internet connectivity and incorporates federated learning techniques to protect data privacy while enabling collaborative model improvement. The project also develops multilingual chatbot interfaces that provide farmers with expert-level guidance and creates comprehensive training programs for agricultural extension workers and researchers across multiple geographies. This project also prepares an AI-versed workforce to serve the U.S. agricultural sector. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Apples are one of the most important fruits worldwide, supporting millions of livelihoods and economies around the globe. However, apple orchards face critical challenges from diseases such as apple scab, fire blight, and powdery mildew, which significantly reduce fruit quality and yield. Farmers traditionally rely on widespread applications of pesticides across entire fields, which is costly, labor-intensive, and environmentally harmful. This project develops a new approach that combines drones (unmanned aerial vehicles, UAVs) and ground robots (unmanned ground vehicles, UGVs) with smart sensors and artificial intelligence (AI) to detect and treat diseases only where needed, using precise amounts of pesticides. The system aims to help farmers reduce chemical use, save labor, and improve economic longevity by identifying early signs of disease and applying treatments in a precise, targeted manner. By working with apple growers in diverse locations, the project also ensures that the technology is practical and accessible for real-world farming. This platform also supports workforce development educational and training activities in robotics and AI. This project addresses the critical challenge of efficiently detecting and controlling diseases in apple orchards, such as apple scab, fire blight, and powdery mildew, which significantly impact crop health and productivity. The research aims to develop a novel, integrated robotic platform combining UAVs and UGVs with advanced sensing and AI technologies. UAVs equipped with multi-spectral and thermal cameras systematically, globally monitor orchard health from above, detecting subtle indicators of plant diseases before they become severe. Concurrently, UGVs operating on the ground use local, detailed sensor feedback to perform precise, site-specific pesticide spraying. Central to this approach is a learning-based model predictive control (MPC) framework, which adapts spray patterns in real time by continuously interpreting sensor data, environmental variables, and disease severity. Additionally, the project establishes a cloud-based IoT platform to integrate and analyze data collected by UAVs and UGVs, providing orchard managers with actionable insights and recommendations through user-friendly dashboards. The project plans to validate the entire system across multiple geographically diverse orchards to ensure broad applicability and robustness. Ultimately, the project aims to significantly enhance disease management efficiency, reduce chemical inputs, and advance precision agriculture practices globally. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The need for materials with improved or new properties is often at the heart of the challenges society faces. Despite massive expansion in experimental capabilities and data, knowledge- and data-centric challenges prevent prediction-driven materials discovery. The NSF AI Materials Institute (AI-MI) aims to propel foundational AI research past the limitations of existing AI algorithms by pursuing materials discovery and conquering knowledge- and data-centric challenges. AI-MI will advance the foundations of artificial intelligence while accelerating the discovery of next-generation materials essential for sustainable energy, electronics, environmental stewardship, and quantum technologies. AI-MI brings together computer scientists, materials researchers, and data scientists to leverage advances in AI technology and materials data to drive use-inspired progress in fundamental AI, catalyzing prediction-driven materials discoveries. By tightly integrating data generation, AI inference, and rapid experimental feedback, AI-MI aims to reduce discovery cycles from months to days and to establish reproducible, reusable workflows for the broader community. AI-MI plans to create the AI Materials Science Ecosystem (AIMS-EC)- an open, cloud-based portal. AIMS-EC will couple a science-ready large-language model with multimodal data streams (experimental measurements, simulations, images, and textual literature). This platform will allow researchers to pose natural-language queries and receive transparent, data-grounded answers, thereby unifying prediction, explanation, and experimental design in a single interface. AI-MI will apply the capabilities of AIMS-EC to discover two-dimensional moire structures with properties suitable for robust qubits, learn descriptors that can guide the design of new superconductors, discover new functional soft materials and mixtures for sustainability, and identify functional peptides for the removal of microplastics. Moreover, AI-MI will accelerate material synthesis through data-driven optimization of film growth and self-driving labs. AI-MI will implement a comprehensive educational program that covers AI and materials science across all levels of instruction. Through educational modules, internship programs, and partnerships, AI-MI will develop an agile talent pipeline to propel advancements at the intersection of AI and materials science for the next generation workforce, ultimately driving advancements nationwide. Moreover, AI-MI will engage with non-academic collaborators, especially in the materials industry, through training programs and internship placements, in addition to research collaborations on problems of practical importance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

State Workforce Development Board

Workforce innovation and skills training grants administered by the AK workforce board

Alabama Department of Economic and Community Affairs (ADECA)

ADECA partners with the Appalachian Regional Commission to provide grants for economic development, workforce training, infrastructure, and community capacity building in Alabama's 37 Appalachian counties.

Alabama Department of Commerce

Administers the AIDT workforce training program, Industrial Development Grant Fund, and various tax incentives for Alabama businesses.

Alabama Department of Commerce

The Alabama Department of Commerce supports small business growth through grants for business expansion, workforce training, technology adoption, and market development initiatives across the state.

NSF

With the support of the Chemical Synthesis (SYN) program in the Division of Chemistry, Professor Dmitry Peryshkov of the University of South Carolina at Columbia is studying the development of metal-free reagents and catalysts for the functionalization of organic compounds. This project will focus on a novel approach to catalysis, the process that underpins the production of the majority of manufactured chemical products, including polymers and pharmaceuticals. Traditionally, many chemical processes depend on precious metals like ruthenium, rhodium, and palladium, which are expensive, scarce, and often toxic. Professor Peryshkov will investigate and design the principles to replace these metals with metal-free systems that can carry out similar chemical transformations in a more sustainable and cost-effective way. These new non-metal reagents will be designed to be "ambiphilic," meaning they can both donate and accept electrons. This dual ability will mimic the behavior of transition metal catalysts and allow breaking and making of chemical bonds in a way typically reserved for metals. This project will involve the use of advanced inorganic and organic synthetic techniques as well as theoretical chemistry methods and will serve as a foundational training ground for undergraduate and graduate students to prepare them for the entry into the future science and technology workforce. The team of graduate and undergraduate students led by Professor Peryshkov will carry out the synthesis of the novel phosphines decorated with boron cluster groups and study of their ambiphilic reactivity. The ability of boron clusters to accepts electrons will be the key to impart electrophilic behavior onto normally nucleophilic trigonal phosphorus centers. The activation of N-H and B-H bonds in metallomimetic manner by the redox-active phosphines will open a pathway for metal-free sustainable new reactions such as hydroboration and hydroamination of unsaturated substrates. Furthermore, the new ambiphilic phosphines will be explored as new reagents for important chemical processes such as cyanation and azidation. A major objective will be to design these systems to be recyclable and catalytic. The properties of ambiphilic phosphines will be controlled via chemical changes to their modular structure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

American Clean Power Association

Supports wind energy research, workforce training, and community benefit programs.