Regents of the University of Idaho

NSF Awards · FY 2026 · 2026-08

Mergers of neutron stars and pairs of supermassive black holes are among the most energetic events in the universe. They can produce gravitational waves, bright flashes of light, and fast jets; neutron-star mergers can also create heavy elements found in planets and living things. Scientists use large computer simulations to understand the hot, magnetized gas around these objects, but many current simulations treat that gas as a perfect electrical conductor. This simplification makes magnetic-field reconnection, a key source of heat, light, and outflows, occur as an uncontrolled byproduct of numerical error rather than through a physically modeled process. This project develops open-source software that lets researchers model magnetic energy release with physically meaningful inputs and reproducible tests. The project advances the national interest by improving computational tools for scientific discovery, strengthening high-performance computing, and training students in computational science. It also broadens participation in science through activities that include research experiences for Deaf and Hard of Hearing students, outreach to rural K-12 students in Idaho, and public visualizations of cosmic collisions through accessible performances and outreach events. This project develops portable cyberinfrastructure for resistive general-relativistic magnetohydrodynamics (GRMHD), the computational framework used to simulate magnetized, electrically resistive fluids in strong gravity. A team of researchers extends the open-source GRHayL library with fourth-order, low-diffusion numerical methods and a resistive GRMHD module with two runtime-selectable conductivity prescriptions. The software uses a vector-potential formulation to preserve the divergence-free magnetic-field constraint, implicit-explicit time integration to handle stiff electric-field relaxation, and diagnostics that compare numerical resistivity with the prescribed physical conductivity. A human-supervised workflow uses large language models to assist code development, testing, documentation, review, and porting while leaving scientific and algorithmic decisions to human researchers. The new capabilities are integrated into the AsterX software system for production simulations and into BlackHoles@Home/GRoovy for single-node development, testing, optimization, and teaching. The project verifies the shared kernels through standard tests, cross-code comparisons, and central processing unit (CPU) and graphics processing unit (GPU) parity tests. Public deliverables include tagged software releases with digital object identifier snapshots, Apptainer containers, verification inputs and reference outputs, documentation, tutorials, and curated benchmark and diagnostic data products. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier Program in the Physics Section of the Directorate for Mathematical and Physical Sciences. 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 Awards · FY 2026 · 2026-07

Research Initiation: How AI Integration Shapes Undergraduate Students' Civil Engineering Professional Formation from Classroom to Career This project will examine how undergraduate civil engineering students develop as future professional engineers in an era when artificial intelligence (AI) is rapidly changing what it means for people to do engineering work. Civil engineers, like other types of engineers, need to understand and use AI-related tools and knowledge in areas including data analysis, modeling, optimization, design, construction monitoring, and infrastructure decision-making. Most undergraduate civil engineering programs are still considering how to best integrate AI into coursework to support students' technical learning and their identity shift from being a student to being a working engineering professional. This project will study how AI-integrated civil engineering coursework influences students’ professional identity, career adaptability, and perceived employability. The findings should help engineering instructors and academic programs better understand how to incorporate emerging AI technologies into courses to better prepare their graduates for AI-driven workplaces, a current national need. The project will further serve the national interest by supporting the preparation of civil engineers specifically who are technically capable, adaptable, and ready to contribute to the nation’s infrastructure development, economic competitiveness, and public welfare by using AI-enabled tools. The project will contribute to the funding program's goals expanding the community of engineering education researchers through the civil engineering research team's structured mentoring from education and workforce development researchers. This project, situated in the Department of Civil and Environmental Engineering at the University of Idaho. It will use a longitudinal mixed-methods sequential explanatory design to answer three research questions: 1) How does integrating AI content in civil engineering courses influence students’ professional identity as engineers? 2) How does integrating AI content in civil engineering courses influence students’ career adaptability in preparing for an AI-driven workforce? and 3) How does integrating AI content in civil engineering courses influence students’ perceived employability? The research team will first collect quantitative data through two online surveys administered at the beginning of Fall 2026 (pre) and the end of Spring 2027 (post) which will include validated measures of engineering identity, career adaptability, self-perceived employability, and various measures of prior experience with AI and civil engineering coursework. The team will analyze the data through descriptive statistics, internal consistency reliability estimates, and longitudinal linear mixed models to examine whether changes across the pre/post period are associated with students’ level of AI-integrated coursework exposure. The team will then collect qualitative data through individual and group interviews after the post survey, purposefully selecting participants based on variability in their survey patterns. The project will mix the quantitative and qualitative data by using survey results to guide qualitative sampling and interview questions, and by connecting statistical patterns with themes from student interviews and focus groups. The team expects project outcomes to provide empirical quantitative and qualitative evidence on AI and professional formation of engineers, which the team will translate into practical guidance for civil engineering instructors, and have relevance to engineering instructors in other disciplines. The team will further share empirically-grounded insights through developing and distributing sample teaching materials, research briefs, and workshop materials based on the outcomes, and publishing through conferences and archival publications to inform researchers and educators nationally. 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 Awards · FY 2026 · 2026-02

Accurately predicting sediment transport is critical for infrastructure management, flood-risk assessment, river restoration, and predictions of habitat for aquatic organisms. Sediment transport predictions are often inaccurate partly because of uncertainties in estimating the onset of gravel motion. Cohesive material such as clay, microbial biofilms, and biological silks from aquatic organisms can affect the onset of gravel motion but are often neglected. A combination of laboratory experiments, river measurements, and modeling will be used to measure and predict the influence of cohesive material on gravel motion. This project will reduce uncertainty in sediment transport predictions, and produce tools that inform sustainable river management, flood mitigation, and restoration. Bedload transport in gravel-bed rivers is often predicted using the critical shear stress that causes the onset of gravel motion. Most practical applications select the critical shear stress for gravel from a nearly order-of-magnitude range or rely on simple functions, which can produce bedload transport predictions that have orders of magnitude in uncertainties. Cohesive materials found in riverbeds can affect gravel critical shear stresses but predictive equations for the effects of these materials are lacking. As part of a NERC-NSF collaboration, this project will: 1) measure the amount of biological and non‐biological cohesion in gravel‐bed rivers, 2) use laboratory experiments to understand how cohesive material alters gravel resisting forces, riverbed structure, and near-bed flow conditions to control critical shear stresses, 3) use refractive-index-matching experiments to understand how changes in riverbed structure from cohesive material affect within-bed flow conditions and near-bed turbulence, and 4) use these results to develop, test, and validate a new mechanistic model for gravel critical shear stresses that includes cohesive effects of clay, microbial biofilms, and biological silks. 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 Awards · FY 2026 · 2026-01

This project aims to serve the national interest by increasing undergraduate STEM students' mathematical creativity and conceptual understanding through the integration of computation into a linear algebra course. The significance of this work lies in its potential to connect students' existing views of coding as expressive and open-ended with more meaningful experiences in mathematics that foster mathematical creativity and understanding. Positioning computing as a mediator of mathematical thinking and creativity is an emerging area of research, especially within the undergraduate research community. Through the development of a set of open-source computational learning modules, this Level I Engaged Student Learning project seeks to broaden participation in STEM and improve the quality of undergraduate mathematics education by developing opportunities for students to engage in computing practices that foster creative mathematical thinking. The importance of this work is the potential to shift how students relate to mathematics, strengthen problem-solving and perseverance skills needed for success in STEM, and provide accessible resources for instructors interested in intentionally integrating mathematics. The project's goals are to (1) develop and assess the impact of a full-semester linear algebra course based on prior piloted modules that use coding as a pedagogical strategy and (2) investigate how such integration affects student learning, creativity, and perceptions of mathematics. The scope of this work includes designing and implementing Jupyter-based activities that encourage prediction, reflection, and debugging, and studying how these features contribute to student learning. Specifically, which features of the computational tasks and computational environment foster mathematical creativity and promote student understanding. The guiding research questions address how computation influences students' (1) conceptual and procedural understanding, (2) opportunities for creativity, and (3) relationship with mathematics. The project will use an instrumental case study approach employing methods of data collection such as classroom observations, interviews, surveys, and student work artifacts. Findings will inform best practices for designing computational mathematics activities as well as new ways of fostering mathematical creativity, and will be disseminated through peer-reviewed publications, conference presentations, and open educational resources. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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 Awards · FY 2026 · 2026-01

This Research Infrastructure Improvement EPSCoR Research Fellows project provides a fellowship to a Research Assistant Professor at the University of Idaho. This work is conducted in collaboration with Dr. Tyler Scott and the Center for Environmental Policy & Behavior (CEPB) at University of California, Davis. CEPB is a research training center for quantitative social scientists from multiple graduate programs. The principal investigator (PI) will learn how to create and maintain a program for environmental policy and management research that scales across individual faculty and students. The PI will also draw from CEPB’s expertise in natural language processing and related data science skills. These tools will be applied to process and analyze novel data about the economics of public lands and local government budgets in the rural west. The novel dataset resulting from this project will provide infrastructure for new research on the impacts of public lands on local government financial conditions. Advances in data science, such as the use of large language models, present unrivaled opportunities to make unstructured data available to answer important questions. As part of this fellowship, the PI proposes to use natural language processing and transformer-based models (i.e., large language models) to collect, aggregate, and standardize local government financial reports and administrative records to build a dataset of local government fiscal decision variables, enabling the PI to explore new questions about the relationship between federal public lands and county-level fiscal outcome variables (tax and expenditure decisions). Further, the study will simultaneously investigate the factors that mediate or exacerbate these relationships including state factors (state-level aid, tax and expenditure restrictions) and local circumvention policies (special districts, redistribution policies). This fellowship will provide core research infrastructure for computational data science within the PI’s department and the data infrastructure to support the development of a Public Lands County Finance Center in conjunction with the University of Idaho’s Institute for Interdisciplinary Data Science. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. 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 Awards · FY 2025 · 2025-12

Minerals are essential to understanding our planet's past and guiding its sustainable future. This project builds on Mindat.org, the world's largest public database of minerals and their global distributions, which receives nearly 10 million visitors per year. With previous support from the National Science Foundation, the OpenMindat project has made mineral data more accurate and accessible for scientists and the public alike. This new phase of work, called OneMineralogy, will expand these efforts into a broader open science ecosystem. OneMineralogy aims to make mineral data and tools available to a wider range of users, including students, educators, researchers, and decision-makers. Its data, tools, and services will help them ask and answer big questions about how Earth's systems have evolved over time. The activities of OneMineralogy will strengthen science education, promote open data sharing, support mineral exploration (including critical minerals), and accelerate discoveries related to planetary science, Earth-life co-evolution, and environmental change. By fostering new partnerships and offering training opportunities, OneMineralogy will empower the next generation of scientists and ensure that mineralogical data benefit society as a whole. Scientifically, OneMineralogy will advance the frontier of data-driven geoscience by developing new data curation strategies, computational tools, and community engagement programs. It builds on the success of the NSF-funded OpenMindat project, which has already improved the quality and accessibility of over 6,000 mineral species records and data from more than 400,000 global localities. OneMineralogy will consist of three major activity clusters: (1) extending and curating data to support a wider range of geoscientific research, (2) building a data science toolbox to enable large-scale analysis of mineralogical systems, and (3) conducting workshops and outreach programs to grow the user community and build capacity. The project will integrate Mindat with other open cyberinfrastructure resources related to paleoenvironments, geomicrobiology, tectonics, and biosignatures. The enriched data and tools will provide strong support to the investigation of Earth's dynamic history through deep time. It will also provide a foundation for interpreting the growing body of mineralogical data from planetary missions to the Moon and Mars. Through these activities, OneMineralogy will create a sustained, open, and collaborative ecosystem to support transdisciplinary research and education in the Earth and planetary sciences. This award by the Geoinformatics program is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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 Awards · FY 2025 · 2025-10

This three-year REU Site: Research experiences for undergraduates in autonomous unmanned (REU-AUV) is hosted by the University of Idaho. Autonomous unmanned vehicles (AUVs), including unmanned aerial vehicles (UAVs such as drones), unmanned ground vehicles (UGVs such as ground robots), and unmanned surface vehicles (USV such as drone boats) are one of the emerging technologies impacting all industry sectors, including the military, precision agriculture, transportation, law enforcement, and insurance businesses. Ten students each year can participate in a 10-week hands-on experience featuring autonomous unmanned vehicles. REU participants will acquire foundational knowledge in automatic control, sensors, artificial intelligence (AI), and robotic swarm technologies while incorporating their cultural experiences and values in problem-solving techniques by leveraging engineering self-efficacy for potential STEM careers. REU students will identify real-world problems and develop a question for information gathering from their perspectives. Participants will complete an online course where they build, code, and deploy a prototype of open-source drones to explore technical solutions for real-world problems. For the rest of the summer, students will complete team projects on-site using AUV platforms and exploring career options. Drawing from multiple disciplines including agricultural engineering, civil and environmental engineering, mechanical engineering, and electrical engineering, participants will engage in cutting edge research and investigate how these emerging technologies impact current and future societal challenges. One example is the use of autonomous unmanned vehicles, encompassing aerial, ground, and surface platforms, and their increasingly vital role for environmental monitoring and sampling. The program will also examine how undergraduate students build self-efficacy by examining how student perceptions and behaviors toward emerging technologies are shaped during the program. multidisciplinary engineering approach to solve real-world problems across rural Idaho. The online and in-person learning activities will address the following: understanding the basic federal regulation and safety guidelines for drone operations (e.g., safe drone flights at the national airspace); exploring the fast-moving technology, such as smartphone sensors for 3D modeling by controlling devices using visualization technology; brainstorming research ideas to solve real-world problems with peers; and completing an online leadership training based around cooperative learning and team-based leadership principles to aid in the process of group collaboration. 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 Awards · FY 2025 · 2025-09

An award is made to the University of Idaho (UI) to enable the Genomics and Bioinformatics Resources Core (GBRC) to acquire an Element Biosciences AVITI24 sequencing platform, a technology that combines traditional sequencing with advanced single-cell and multi-omics capabilities. This instrumentation will power transformative research with direct societal impact, including efforts to improve agricultural resilience, protect endangered species, and advance biomedical discoveries related to infectious diseases. As the first platform of its kind in Idaho and the inland Pacific Northwest, the AVITI24 will strengthen regional scientific infrastructure, foster new collaborations, and expand access to high-quality genomics tools across institutions. The research will also support training initiatives that equip students and early-career scientists with hands-on experience in cutting-edge genomics and bioinformatics, helping build a skilled STEM workforce. The research enabled by this platform at UI will significantly expand the capacity for innovative work across disciplines such as biomedical science, microbial ecology, conservation biology, and evolutionary genetics. The AVITI24’s high-throughput sequencing and single-cell profiling features will allow researchers to investigate gene expression, microbial communities, and cellular interactions at unprecedented resolution. These capabilities will enhance ongoing studies of species adaptation, genetic disease mechanisms, and host-pathogen dynamics while also promoting cross-disciplinary collaboration. By fostering technological advancement and generating new knowledge, this investment will elevate research competitiveness at UI and broaden the region’s contribution to national scientific priorities. 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 Awards · FY 2025 · 2025-09

Spillover of infectious diseases from wildlife to humans and livestock is a pervasive risk to the health and welfare of human populations around the world. Effective management of this risk is facilitated by early detection of changes in the frequency of spillover events. This research will develop new mathematical models and statistical methods that allow changes in the rate of spillover to be detected from the fossil record of past infection that remains imprinted on human and animal immune systems. The general methodology developed by this project will be rigorously tested using simulated data and applied to Rift Valley fever virus, a pathogen that poses a high risk of global expansion with potentially devastating consequences for human health and agriculture. Work on this project will train students in cutting edge mathematical and statistical methods and support an international workshop where software developed by the project will be introduced and instruction on its use provided. Predicting how zoonotic infectious diseases change over time is a fundamentally important challenge with few general mathematical solutions. Central to addressing this problem is disentangling historical changes in the rate or “force” of spillover from background biological processes, such as age-specific infection and wanning immunity, which can cloak or mimic the signal of temporal change. Existing statistical methods to infer historical changes in the force of spillover for zoonotic pathogens rely on piecemeal solutions tailored to specific scenarios, ignore interacting background processes, use only single immunological markers, and have failed to rigorously evaluate parameter identifiability. To fill this gap, this project will develop a general mathematical framework describing the probability that an individual is in a specific multivariate immune state as a function of age and time using a coupled system of partial differential equations (PDEs). Approximate and numerical solutions to this system of PDEs will enable a Bayesian statistical framework for inferring recent historical changes in the force of spillover in the presence of alternative biological processes. Testing this statistical framework using extensive, biologically realistic simulated datasets will allow the identifiability of historical change in force of spillover to be evaluated. Application of this methodology to Rift Valley fever virus, a pathogen with significant pandemic potential, will determine whether increasing case counts in East Africa result from fundamental shifts in disease epidemiology or from increased disease surveillance. 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 Awards · FY 2025 · 2025-09

Preschool and kindergarten-aged children are still developing the skills needed to reflect on and manage their own thinking, a process often referred to as metacognition. Without strategic support from their teachers, young children may struggle to make sense of inquiry-based science activities and possibly form enduring misconceptions that may hamper future science learning. Yet, many teachers are unfamiliar with the metacognitive processes or how to intentionally facilitate their development. This project explores both how to improve early childhood teachers' understanding of metacognition and develop strategies to guide teachers in using language and feedback to more effectively support emerging metacognition and science learning in young children. Working with teachers and children in rural Idaho, the project includes classroom observations, experimental studies on teacher-child interaction, and the development of a professional development program designed to help teachers strengthen young children's reasoning and reflection. The findings will contribute to improving science instruction in early childhood and offer practical guidance for teacher education and professional learning. The project includes three interconnected research studies designed to explore and enhance how early childhood teachers support young children's science learning through metacognitive development. Study 1 will assess current science instruction in rural Idaho classrooms by collecting data from 55 teachers and 330 children, including classroom observations, teacher-child interactions, and child science assessments. Quantitative and qualitative analyses will identify features of classroom environments that support or hinder metacognitive engagement. Study 2 uses an experimental design with 121 children to examine feedback type-either focused on their performance or aimed at helping them reflect on their thought processes- delivered either during or after structured science activities influence performance. Researchers will analyze how these variations influence children's ability to reflect on their own thinking using coded video data and statistical comparisons. Study 3 builds on these findings to design and evaluate a year-long professional development program for 25 teachers, incorporating online modules, curriculum implementation, and in-person coaching. Throughout the program, the research team will gather classroom observation data, teacher and child surveys, and interview responses to evaluate changes in teaching practices and children's learning. Findings from all three studies will inform both theory and practice by identifying effective ways to support teachers' conceptions and awareness of metacognition as well as how they can use language and feedback to support young children's metacognitive growth in science. The CAREER award is funded by the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed project. 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 Awards · FY 2025 · 2025-09

Gravity was the first force mathematically described by scientists as an explanation of the motion of the moon, planets, and stars. Hundreds of years later, gravity is still considered to be the most poorly understood force. It cannot be consistently described across different theories in physics, and even its strength is poorly known. The Newtonian constant of gravitation, which quantifies the strength of gravity, is perhaps the most poorly measured fundamental property of the universe. This project seeks to greatly improve our knowledge of this constant with the most significant change in technique since the experiments of Henry Cavendish in 1798, who used a torsion balance for his measurements. The measurement funded by this award may be of value across many areas of physics and astronomy, and the new experimental tools being developed may lead to new instruments for measuring extremely small forces and accelerations for use in studying the Earth and for inertial navigation. In addition, the attention to detail required in these experiments makes them an exceptional training ground for tomorrow's STEM workers. This project aims to resolve discrepancies in measurements of the Newtonian constant of gravitation, G, advancing our understanding of fundamental forces and potentially uncovering new physics. The approach is to levitate a graphite composite test mass in ultra-high vacuum. Changes in the oscillation frequency of the test mass, induced by carefully positioned field masses, are used to determine G. Eliminating mechanical suspensions minimizes systematic errors, while analysis of the trajectory enables precise modeling of higher-order oscillation modes in the test mass's motion. The goal is to provide an independent and transparent measurement of G with a precision of 10 parts per million, offering valuable insights into resolving discrepancies in existing measurements and paving the way for even more precise determinations. The experiment emphasizes simplicity in design and openness to encourage broader participation from the scientific community. All data and analysis tools will be made openly available to facilitate independent verification. By carefully addressing systematic uncertainties and sharing results transparently, this project aims to contribute constructively to the broader effort of refining the measurement of G, while demonstrating methods with applications in precision metrology and gravitational physics. The project's novel approach, utilizing magneto-gravitational trapping technology, represents a significant leap in precision measurement techniques, with implications for fields ranging from fundamental physics to precision sensing. Beyond improving the accuracy of G, this work could contribute to experiments exploring new interactions, such as potential couplings between dark matter and ordinary matter, as well as to precision accelerometry in space. 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 Awards · FY 2025 · 2025-09

The three-year REU Site: Intelligent Manufacturing for a Sustainable Energy Future Research Experiences for Undergraduates is hosted by the University of Idaho (Center for Advanced Energy Studies) and partners with the Idaho National Laboratory. A 10-week summer research program for 8 undergraduates each year will engage participants with hands-on research skills, networking with laboratory scientists and interns, utilizing world-class cutting-edge equipment, and developing their STEM identity and literacy. REU participants will have the opportunity to conduct hands-on research, present their research results at the INL internship poster session, and co-publish with their REU mentor. Student participants can look forward to working in an active and engaging collaborative research center to advance research at the intersection of intelligent manufacturing and sustainable energy. The REU experience will help educate the next generation of scientists, engineers, and STEM leaders and provide professional development opportunities for careers in the energy sector. The program is structured to identify new knowledge about the relationship between academic and government institutional research activities impacting the REU experience. REU students will present research results at the Idaho Conference for Undergraduate Research and INL internship poster session. The objectives will enable innovative outcomes in the development of the future intelligent manufacturing and sustainable energy workforce. Specifically, REU students will: expand their knowledge and learning at the intersection of intelligent manufacturing and sustainable energy in support of STEM workforce development; engage in authentic research opportunities to enhance STEM identity and literacy; improve their technical skills while also expanding their professional network in support of future career objectives. Another objective is to increase the awareness of effective mentoring practices among faculty and government laboratory scientists to ensure a positive and lasting impact on REU participants. This Site is supported in part by funds provided to the National Science Foundation by the Semiconductor Research Corporation. 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 Awards · FY 2025 · 2025-09

An award is made to the University of Idaho to engineer an instrument that uses advances in artificial intelligence to identify small mammal species, discriminate among individuals, and measure body weight and morphological traits in real-time. Despite their abundance and applied importance as agricultural pests and reservoirs of human infectious disease, the methods used to study small mammals in the wild have changed little over time and generally require that individual animals are captured and handled by well-trained and permitted research staff. By removing this requirement, the instrument developed by this project will reduce research costs and increase the efficiency, pace and extent of data collection in both the pure and applied biological sciences. Beyond advancing research instrumentation, work on this project will enhance science education at the interface of artificial intelligence, engineering, and biology through partnerships with K-12 schools and integration into senior design projects for engineering students at the University of Idaho. By enabling automated monitoring of wild animals, the proposed work will enhance biological research capacity in three ways. First, the cost and labor required for data collection will be vastly reduced, increasing sample sizes and the geographic and temporal scope of study. Second, regulatory obstacles and safety concerns to studying threatened or endangered species will be reduced because animals need not be captured and handled. Third, because the instrument is capable of autonomous operation and data transmission for extended periods of time, it will facilitate research in harsh or dangerous environments. Instrument adoption by researchers in the biological sciences will be facilitated by instrument loan to Idaho Department of Fish and Game for use in small mammal surveys and through the donation of ten instruments to a non-profit selected through a merit-based application process. 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 Awards · FY 2025 · 2025-08

The project intends to expand K-12 and college-age student participation in engineering for all Idahoans. Not all Idaho classrooms have access to robust STEM programming. The project represents an opportunity to partner with K-12 and rural community stakeholders to learn where pathways to engineering education and workforce can be strategically built in EPSCoR states. The engineering and computer science supply and demand within the Idaho workforce is incongruent – with a shortage of educated and skilled engineers. Not only does Idaho need more engineers, but the state also needs more students from a variety of geographic locations, urban and rural, to solve engineering and security problems. The broader impact of this project, if successful, is on all Idaho students and regions in a state where not all students have opportunities to learn about engineering and computer science. This research project extends the Design Thinking Framework theory. A gap in the literature is the lack of studies and examples of collaborative projects using the framework. Though there is extensive research in various pedagogies around respect and self-reflection, which is the foundation to this framework, few published studies have shared the outcomes of using this community engagement process. This project develops co-designed engineering pathways through the integration of Design Thinking and collaborative research activity. This project is intended to 1) co-create and engage participants in pathways to engineering state-wide, and 2) co-design research activity toward the creation of a Center for Pathways to Engineering at the University of Idaho. To drive strategies and pathways for engineering education and the future STEM workforce, there is a need to understand the learning gap for rural students. Participants from all regions of Idaho will be encouraged to collaborate in research activities. 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 Awards · FY 2025 · 2025-08

Plant parasitic nematodes cause an estimated US$80 to 157 billion in annual crop losses worldwide. The most economically important plant parasitic nematodes include root-knot nematodes from the genus Meloidogyne and cyst nematodes of the genera Heterodera and Globodera. Both root-knot nematodes and cyst nematodes, as sedentary endoparasites, penetrate the host root to establish a feeding site (a giant cell or syncytium), where they settle down and feed through subsequent sedentary life stages. Plants in turn use a sophisticated innate immune system to perceive and defend themselves against the invading nematodes. This project aims to address a key question related to plant resistance to nematodes: how do plants detect nematode-derived small-molecule signals, specifically ascaroside 18 (Ascr18), to activate their immune system and fight infection. Besides elucidating the mechanisms underlying potato resistance to the notorious potato cyst nematode Globodera pallida, this project will also support the training of high school, undergraduate and graduate students. By connecting cutting-edge molecular biology with real life agriculture problems, this project will motivate and engage students in plant biology research and inspire them to become the next generation of leading plant scientists. Plants have evolved pattern recognition receptors (PRRs), which detect pathogen-associated molecular patterns (PAMPs) as a basal layer of immune response to activate pattern-triggered immunity (PTI) against invading pathogens and pests. Extensive studies over the past two decades have identified numerous PAMP-PRR-mediated PTI signaling pathways against numerous types of pathogens. However, the molecular basis underlying the activation and signaling of PTI in plant-nematode interactions is largely unknown. It has been recently found that the potato (Solanum tuberosum) PRR, Nematode-Induced Leucine-Rich Repeat Receptor-Like Kinase 1 (StNILR1), recognizes the nematode-associated molecular pattern (NAMP) ascaroside 18 (Ascr18), thereby conferring resistance to cyst nematode Globodera pallida. In addition to canonical PTI signaling, StNILR1 and its coreceptor Brassinosteroid Insensitive 1-Associated Receptor Kinase 1 (BAK1) modulate RNA metabolism machinery to down-regulate genes essential for the development of nematode feeding site (syncytium), thereby interfering with nematode parasitism. This project will leverage these preliminary findings to elucidate the molecular basis underlying Ascr18-triggered immune signaling in potato. Specifically, the project will elucidate the mechanistic basis of Ascr18-StNILR1/StBAK1-mediated immunity, determine the role of the StCAF1 deadenlyase in potato PTI responses, and dissect the transcriptome reprogramming caused by Ascr18. The project has implications for U.S. agriculture through improving potato production and enables workforce development through training of high school, undergraduate and graduate students. 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 Awards · FY 2025 · 2025-08

Creating a software replica of all actionable knowledge about the immune system, an immune digital twin, has the potential to provide insights into many unknowns in biology, human health, and disease. The goal of this project is to initiate a construction of generic modules of immune cells and assemble them across multiple scales, enabling their reuse in response to various pathogenic insults. An immune digital twin could predict how an individual’s immune system may respond to an infection, how inflammation becomes harmful, or how therapies work before they are even tested on patients. To this end, this CAREER project will focus on two fundamental questions: What level of mathematical abstraction and granularity is required to represent an immune digital twin, and how can diverse data types — categorical, qualitative, and quantitative — be integrated to calibrate and validate its components? These research efforts will be complemented by the development of summer research programs for undergraduates aimed at introducing students to mathematics and STEM applications through the lens of digital twins. Courses and publicly available code will be specifically designed for undergraduate students interested in research, with the goal of bringing more students into digital twin science. To address the project's research questions, the first objective will bring forward the mathematical theory and concepts necessary to conceive a generic digital twin of an immune cell. The mathematical foundations of stability, identifiability, and well-posed immune digital twins will be developed. The second objective will integrate data from different labs into multi-scale algorithms to calibrate immune digital twins. The causal effects of these twins and calibration algorithms will be evaluated in a ground truth model that will provide a transparent setting for interrogating the proposed research questions. The third objective will leverage the previous two to create a blueprint of an immune cell. Ultimately, the research and educational activities of this project will contribute to national prosperity by developing new computational methods that advance digital twins capable of predicting immune responses in a virtual environment. 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 Awards · FY 2025 · 2025-08

Ecotron facilities, crucial for studying ecosystem responses to urban change under controlled conditions, suffer from fragmented data practices. Varying data formats, metadata standards, and access protocols hinder data sharing and comparative analysis across institutions. This project addresses these challenges by developing a unified national cyberinfrastructure specifically designed for ecotron-generated data. The Collaborative Open Framework for Accessible Interoperable Research (CO-FAIR) project establishes a collaborative network of ecotron facilities. The project focuses on developing standardized metadata schemata, common ontologies by leveraging and extending existing standards, and robust data access protocols, adhering to principles that make data Findable, Accessible, Interoperable, and Reusable (FAIR). The technical architecture includes a centralized metadata discovery portal, distributed data storage solutions, and Application Programming Interfaces to enable seamless data integration and analysis. Data ingestion pipelines will automate data flow from participating ecotrons, with the University of Idaho's Deep Soil Ecotron serving as a primary development and pilot implementation site. The system is built on open-source technologies to ensure scalability and portability. By standardizing data management and fostering collaboration, CO-FAIR enhances the value and utility of ecotron research. This project will transform disparate ecotron datasets into a cohesive, accessible national resource, accelerating discoveries in soil science, ecosystem ecology, and sustainable agriculture. It will enable large-scale comparative studies, enhance the reproducibility of ecological research, expand access to valuable experimental data, and serve as a model for open science practices in other scientific domains. 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 Awards · FY 2025 · 2025-08

This Faculty Early Career Development (CAREER) award will support research that looks to develop a sustainable wood protection system using a naturally derived, environmentally friendly antimicrobial compound—epsilon-polylysine (EPL). The project seeks to address the urgent need for safer, more sustainable alternatives to conventional wood preservatives, such as copper-based treatments, which pose environmental and health risks due to their toxicity and leaching behavior. The proposed research contributes to national interests by working to advance green building materials that promote long-term carbon storage and reduce infrastructure maintenance costs. This work also aligns with the NSF mission by increasing the understanding of how EPL interacts with the wood system and enhancing national well-being through innovation in sustainable infrastructure. Additionally, the project integrates impactful educational initiatives that engage K–12, undergraduate, and graduate students, aiming to inspire the next generation of Wood Science and Engineering (WSE) professionals. By combining cutting-edge research with broad educational outreach, this CAREER project fosters scientific advancement and workforce development to support a more resilient and sustainable future. This project aims to develop a new wood preservative system, where a bio-based preservative (EPL biosynthesized by bacteria Streptomyces albulus) with broad biological activities but low environmental hazards will be valorized and used for wood treatment induced by Maillard reaction. The specific aims are to 1) determine key factors that affect the durability of EPL-treated wood induced by the Maillard reaction for exterior applications; 2) understand the long-term durability, mechanical and fire performance of EPL-treated wood through summer Build & Learn experience for real applications; 3) elucidate antimicrobial mechanisms of EPL-treated wood; 4) examine sustainability of EPL-treated wood via ecotoxicity tests, economic and environmental viability analysis; 5) integrate sustainable wooden infrastructure material protection research with experiential learning and near-peer mentoring to engage students, from local middle school and high school students to graduate students, and cultivate interest in the field of Wood Science and Engineering and beyond. This work will lay the foundation for a new class of green, bio-based wood protection technologies and contribute significantly to sustainable and resilient wooden infrastructure materials. 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 Awards · FY 2025 · 2025-08

In this CAREER project, jointly funded by the Chemical Mechanism, Function, and Properties (CMFP) Program of the Chemistry Division and the Established Program to Stimulate Competitive Research (EPSCoR), Professor Sebastian Stoian of the Department of Chemistry at the University of Idaho is developing new iron-based, multifunctional magnetic materials with tailored properties. The goal of this research is to map out subtle interactions between the building blocks of these materials. This project seeks to advance our knowledge of complex materials encountered in magnetism and catalysis, addressing important challenges in quantum computing and energy production. The project provides advanced research training to graduate and undergraduate students and includes a curriculum development plan for enhancing the computational chemistry competencies of undergraduate students. Finally, research experiences for teachers will also be part of the funded project as outreach activities aimed at boosting the STEM motivation of high-school students throughout Northern Idaho. Chemical species featuring metal ions supported by redox active ligands play critical roles in the catalytic cycles of many enzymes and synthetic systems. Yet, the oxidation and spin states of the metal sites and their supporting ligands are often difficult to assess. In this project, Professor Stoian’s team will use a spectroscopy-guided approach to synthesize and investigate a series of iron compounds with selected redox-active ligands. To elucidate the intricate interplay between spin and charge degrees of freedom in such species, the planned research relies on the unique capabilities of field-dependent Mössbauer spectroscopy, complemented by electron paramagnetic resonance and computational studies, to probe the electronic structure of iron sites in complex chemical environments. The target materials could exhibit a number of desirable properties including magnetic bistability and oxygen activation, as well as quantum entanglement of local metal spin states induced by their interaction with open-shell, redox-active ligands. 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 Awards · FY 2025 · 2025-07

The National Science Foundation (NSF) EPSCoR Graduate Fellowship Program (EGFP) supports EGFP-designated institutions and programs in EPSCoR jurisdictions by providing funding for graduate fellowships for new or continuing EGFP-eligible applicants. EGFP supports a total of three years of stipend and associated cost-of-education (COE) allowance for each NSF EPSCoR Graduate Fellow. This award at the University of Idaho will support nine (9) EPSCoR Graduate Fellows whose research will align with the unique goals and programs supported by the Directorate for Biological Sciences (BIO), Directorate for Computer and Information Science and Engineering (CISE), Directorate for Engineering (ENG), Directorate for Geosciences (GEO), Directorate for Mathematical and Physical Sciences (MPS), Directorate for Social, Behavioral, and Economic Sciences (SBE), Directorate for STEM Education (EDU), and the Office of Integrative Activities (OIA). There is a national need to develop and advance innovative approaches to increase the resilience of communities and landscapes in the United States to the impacts of wildfires. Several recent syntheses have called for multidisciplinary research to solve these complex problems. This project will directly address these challenges by cultivating the next generation of research leaders in innovative approaches that advance knowledge of community and landscape resilience in the face of changing wildland fire regimes. The project will leverage the extensive institutional expertise and multi-disciplinary leadership in wildland fire science at the University of Idaho to guide EPSCoR Graduate Fellows in advancing three research themes, each seeking to improve our knowledge of how fire impacts communities and landscapes: 1) mechanistic knowledge and tools; 2) dynamic ecosystem feedback and trajectories; and 3) integrated human-environment systems. The Fellows will be mentored by faculty whose research covers the full range of wildland fire science research, who have access to graduate certificates, and who have access to resources within two current NSF EPSCoR projects where wildfires are a significant focus. The project's intellectual merit includes increasing knowledge associated with how communities in the United States can become more resilient to the impacts of wildfires. The project will also assess the efficacy of a novel approach to create graduate Fellow cohorts to solve multidisciplinary problems, where each Fellow will be enrolled in one of three University-wide interdisciplinary doctoral degrees: Bioinformatics and Computational Biology, Environmental Science, and Water Resources. This will enable the project to engage the Fellows in a unique combination of existing areas of academic research strengths in the geosciences, biological sciences, social sciences, and resilience science, and build their strengths and research capacity in computational modeling, machine learning, and artificial intelligence, to provide analytical outcomes to proactively address the impacts of wildfires on energy-water systems through the lens of weather, population, and technological change. In terms of broader impacts, the project will engage Fellows within a dynamic mentoring program and a robust and institutionalized graduate mental health support program system. Additionally, Fellows will have the opportunity to participate in the Environmental Education and Science Communication Graduate Certificate program that will enable them to develop transferable skills in science communication, outreach, and interdisciplinary thinking. Fellows will also help the research team further develop novel areas of research (e.g., pyro-aerobiology, pyro-ecophysiology) and help create new sub-disciplines of wildfire science and engineering that will occur at the intersection of increasing community resilience. 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 Awards · FY 2025 · 2025-07

Modern society is increasingly dependent on regional distribution networks to electric power long distances. However, these systems are vulnerable to disruptions from fires, windstorms, snowstorms, or even cyberattacks. Improving community resilience requires installation and management of localized energy sources. To ensure stable operation both when connected to the larger power grid and when isolated during disturbances requires an integrated control and communication system. This project enables planning for a large-scale research project in the areas of advanced microgrids to meet future community needs and driving scientific engagement between researchers, local stakeholders and industry to increase resilience of electric power for communities. This project seeks to form an institutional framework for multiple universities in EPSCoR jurisdictions, national labs, and industrial partners to collaborate, to research and design resilient next-generation power systems. The research goal is to integrate and manage energy from varied sources including energy storage, solar, wind, geothermal, and nuclear sources efficiently, reliably and securely. By engaging in advanced and applied research, the team advances new microgrids that offer low-cost, disaster-resilient, and cyber-secure power generation that will enhance community resilience to man-made and natural disasters and enhance workforce development in communities. 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 Awards · FY 2025 · 2025-07

Poly- and perfluoroalkyl substances (PFAS), also called “forever chemicals,” are a serious problem in water across the United States. These toxic chemicals do not break down naturally, build up in people and animals, and are linked to major health risks. Current water treatment methods cannot destroy PFAS because of their very strong carbon-fluorine bonds. This project will develop and test a new method called Continuous-flow Liquid-phase Plasma Discharge (CLPD). The CLPD system creates powerful chemical reactions in water to break apart PFAS completely, without making new harmful byproducts. If successful, this could be a simple, chemical-free, and low-energy way to clean PFAS from water. The project integrates educational outreach and workforce development components, including new course offerings, research opportunities for high school, undergraduate, and graduate students, and dissemination to stakeholders through professional development resources. This project aims to engineer and investigate a Continuous-flow Liquid-phase Plasma Discharge (CLPD) process designed for the complete defluorination and mineralization of poly- and perfluoroalkyl substances (PFAS) in water. PFAS represent a persistent class of contaminants due to their strong carbon-fluorine (C-F) bonds, which resist conventional chemical and biological degradation, leading to bioaccumulation and significant health concerns. While nonthermal plasma discharge shows promise for degrading recalcitrant pollutants, existing gas-phase plasma approaches cleave carbon-carbon (C-C) bonds in dissolved PFAS, generating shorter-chain perfluoroalkyl acids (PFAAs) like PFBA and PFPeA that resist further degradation but offer limited defluorination. The CLPD process overcomes this limitation by enabling in-situ generation of highly reactive species, particularly hydrated electrons, within the PFAS solution without requiring bulk gas phases. These hydrated electrons can selectively attack and break the strong C-F bonds in the liquid phase without preferentially cleaving C-C bonds, thereby achieving defluorination without producing short-chain intermediates. Preliminary results with CLPD demonstrated 62.8% defluorination of perfluorooctanoic acid (PFOA) with no detectable shorter-chain byproducts within one hour without catalysts or gas, and 99% defluorination of PFAS in aqueous film-forming foam (AFFF) with minimal argon flow and high energy efficiency. The objectives for this project are: a) Establish theoretical modeling of CLPD in PFAS solutions to quantify energy delivery, reactive species generation (focusing on hydrated electrons), and plasma-liquid interactions; b) Investigate the fundamental mechanisms and kinetics governing the interaction of plasma-generated reactive species with PFAS molecules, specifically targeting C-F bond cleavage while avoiding C-C scission; and c) Elucidate the detailed reaction pathways leading to quantitative defluorination and mineralization of PFAS (including long-chain precursors and shorter-chains) under CLPD treatment and define optimal process control strategies. The expected outcomes include theoretical and thermodynamic models for CLPD energy transfer, mechanistic understanding of species-specific defluorination reactions, and optimized process control strategies. These insights will enable the design of plasma-based systems that can be scaled from laboratory to field for real-world PFAS remediation applications. The findings will contribute broadly to the fields of plasma chemistry, environmental engineering, and advanced oxidation/reduction technologies for emerging contaminants. 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 Awards · FY 2025 · 2025-06

This award supports US-based participants in the 21st International Conference on Permutation Patterns, which will be held in July 2025 at the University of St. Andrews in Scotland. Permutation patterns is the area of research in mathematics and computer science that studies how small patterns can occur as parts of larger patterns, particularly in rearrangements of objects in a line. There are applications in biology, particularly in the study of DNA rearrangements and amino acid sequences. This conference is part of a series that has been held annually since 2003 (with virtual substitutes during the COVID pandemic), rotating mostly between different institutions in North America and Europe. Each year, the conference features two plenary talks and approximately thirty-five contributed talks, providing researchers with the opportunity to present and discuss their results, while also offering newcomers to the study of permutation patterns a broad perspective on the subject as a whole. This year, there will also be an introductory workshop aimed at graduate students. The conference offers a platform for collaboration between junior and senior researchers. There will be time set aside for open problems to be discussed by the entire community. The conference attracts broad participation, including significant numbers of faculty from primarily undergraduate institutions. The conferences will feature research on permutation patterns and their applications. Permutation patterns is an interdisciplinary area with roots in both theoretical computer science and combinatorics. Major themes include the structural properties of permutation classes, asymptotic behavior from both enumerative and probabilistic points of view, extremal questions, algorithmic and decidability questions, and automated theorem proving and discovery. There are applications to other areas of mathematics and computer science as well as to biology. The conference website is at https://sites.cs.st-andrews.ac.uk/pp25/ 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 Awards · FY 2025 · 2025-04

This project focuses on the combustion efficiency of fuels with high hydrogen content such as hydrogen, methane, and ammonia. High hydrogen content fuels can produce superior ignition and combustion performance, but the reactivity of these fuels varies considerably. Understanding the combustion chemistry and kinetics of reaction for each of these fuels is important, especially since they are often blended in practice. Furthermore, these fuels can be converted to thermal energy not only by standard combustion systems but also by catalytic means. The project will use a Rapid Compression Expansion Machine (RCEM) and a Thermo-Catalytic Reactor (TCR) to explore combustion chemistry, both with and without a catalyst. Results from the project will advance scientific knowledge about high hydrogen containing fuel mixtures that can help satisfy growing energy and transportation needs. Outcomes will aid the development of advanced high-efficiency combustors for ground-based power generation applications and aero-propulsion applications. The project will also support training to expand the science and engineering workforce to better utilize energy resources. This project will develop a comprehensive understanding of the combustion kinetics of hydrogen-rich fuels for gas-phase and catalytic combustion. Fuels to be studied include pure components hydrogen, ammonia, methane, and their binary blends. Fundamental knowledge related to autoignition, combustion byproduct speciation, ignition energies, and reaction kinetics will be generated using well-characterized and novel experimental approaches. The gas-phase combustion responses of interest include ignition delay times and time-resolved pre-ignition species evolution. An RCEM will be used to obtain the ignition delay times and time evolution of stable intermediate species for homogeneous fuel-air mixtures. The catalytic combustion of these fuels will be investigated using a platinum TCR. Experimental results on minimum catalytic ignition energies, spatio-temporal evolution of the ignition process, and the product distribution for varying residence times and equivalence ratios will be obtained. The results from this work will be useful in the design of advanced combustion systems and provide targets for the development and validation of gas-phase and surface combustion reaction mechanisms. 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 Awards · FY 2025 · 2025-02

Accurate water resource predictions are critical for the nation’s water security, especially during extreme events. Today, communities are experiencing increasingly frequent floods and droughts, heat waves, and wildfires, all of which will impact vegetation behaviors and drive land use/cover changes. As such, predicting the resulting impacts on water budget components is fundamental to enhancing the nation’s preparedness for water-related hazards and water scarcity under a changing climate. However, virtually all land models ignore a substantial volume of plant-available water stored in unsaturated, weathered bedrock. This oversight can lead to biased predictions of ecosystem water consumption and downstream water budget components, jeopardizing the nation’s water-food-energy security. The research objective of this proposed project will be to directly address this knowledge gap by implementing an innovative approach within the Community Land Model Version 5 (CLM5) to represent vegetation's uptake of rock moisture and subsequently evaluate its impact on water budget simulations across the continental U.S. (CONUS). This fellowship will enable the PI to establish a mentor-mentee relationship with Dr. Sean Swenson, a prominent hydrologist and core developer of CLM5, and to receive specialized training and access to state-of-the-art land modeling equipment at the National Center for Atmospheric Research (NCAR). Specifically, the PI proposes that modeling plant access to rock moisture will refine simulations of water budget dynamics. To investigate this hypothesis, the PI will visit NCAR for two summers, where Dr. Swenson will mentor the PI in advanced techniques for modifying land models, focusing on CLM5—a leading-edge land model widely used to study the role of land in climate and weather. To enable vegetation water uptake from the weathered bedrock layer, this fellowship project will truncate the root profile by the position of the unweathered bedrock, which will stimulate root growth into the hydrologically active bedrock layer and account for the ability of the vegetation to extract water stored in weathered bedrock. The team will run the original CLM5 and the rock moisture configuration with the same forcing data. Then, they will compare the performance of the two models, assessed by their accuracy in simulating observations of evapotranspiration and streamflow, as well as their responses to climatic extremes. This collaborative endeavor will advance the understanding of land-atmosphere interactions and improve the predictive capabilities of land models, ultimately contributing to more accurate assessments of water availability and environmental resilience across the CONUS. The broader impacts of this fellowship will be further extended through installing CLM5 on Idaho’s supercomputer, organizing a workshop for academic professionals and students, and crafting a hands-on CLM5 course. Together, these efforts will enhance Idaho’s hydroclimate modeling infrastructure, enhance research capabilities, empower STEM researchers and students, and elevate Idaho’s standing in national research endeavors. 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.