2025 - 2026 SURE Projects

Project Number

Project Title

Brief Project Description

Job Duties

Required/Desired Skills

1

AI-enabled synthetic protein design for efficient rare-earth separation

Rare earth elements (REEs) comprising the lanthanide chemical group play an irreplaceable role in modern technologies and are critical for modern electronics. Despite their name, REEs are actually relatively abundant in the Earth's crust; however, they are not sufficiently concentrated for economic mining, extraction, and separation of individual REEs, making this an essential area for research and development. An intriguing option is the use of biological systems, which can be ideal for separations due to their impressive specificity as well as compatibility with environmentally friendly processes. The overarching goal of this proposal is to use an AI-driven approach to develop a synthetic biology-based solution to address this real world problem, and create targeted protein/peptides with enhanced REE affinity and selectivity. The project will employ a closed-loop format, combining AI-enabled protein structure-sequence predictor tools with state-of-the-art enhanced sampling molecular dynamics simulations to design and validate de novo REE binding proteins/peptides.

• Using AI-based tools such as Alphafold 2/3 and ProteinMPNN for identifying protein candidates,
• Scripting in Python for analysis,
• Using ARCC HPC (Medicinebow or NCAR supercomputing clusters) for state-of-the-art enhanced sampling molecular dynamics computer simulations,
• Reading publications,
• Maintaining lab logbooks and eventually writing manuscripts,

Desired Skills:

• Scripting experience in any language (C,C++, fortran, python) for transferable knowledge,
• Experience with linux based operating system,
• Interest in proteins and biomaterial applications,
• Curiosity and motivation to learn,

2

Understanding The Ideological Influence of Technologies in Society

Working in the Human Relationships with Technology Lab with Dr. Kenneth Hanson on one of two (or both) projects depending on student experience and interest. Project 1: Understanding the Motivations and Experiences of AI Chatbot Companion Users — an interview study of people who use AI chatbots to fulfill personal, intimate, and emotional needs and desires. Assist with data collection and data analysis. Project 2: Mapping the Ideological Diversity of Podcast Guests Using Social Network Analysis — a social network analysis project examining the longitudinal change of guest composition, political views, viewership, and other relevant information of popular podcasts. Assist with data collection and data analysis.

• Data collection (qualitative, quantitative, mixed methods),
• Data cleaning,
• Data analysis (qualitative, quantitative, mixed methods),
• Literature review work (collating relevant papers, summarizing research findings, identifying gaps in literature, identifying suitable publication outlets),

Required Skills:

• Strong communication skills,
• Strong interpersonal skills,
• Ability to meet deadlines,
• Independent work ethic,
• Attention to detail,
• Familiarity with social science research practices and ethics

Desired Skills:

• Social network analysis (ERGM, TERGM),
• Qualitative coding,
• Sensitivity analysis,
• Sentiment analysis,
• Network visualizations,
• Interview techniques for data collection,

3

Predictive Monitoring Human Operational Performance in Simulated Stressful Spaceflight

The simultaneous exposure to multiple spaceflight stressors such as space radiation, distance from earth, isolation and confinement, and altered gravity significantly impacts human operational performance, posing a great risk to astronauts’ health and their mission objectives. Thanks to the advances in wearable biosensor technology, multiple biological responses to the encountered stressors can now be detected and tracked in real time by wearable biosensors for timely assessment of the cognitive-motor state of astronauts. However, there is limited research on how to computationally integrate multimodal sensing data to identify sufficient biomarkers for accurate and timely assessment of the cognitive-motor state of astronauts. In this proposed research, we will investigate the combined effect of multiple stressors simulated in 3D immersive virtual reality (VR) on astronaut performance. Participants will wear multiple biosensors and perform an operational task simulated using an inversion table and a VR headset, while a real-time suite of biobehavioral data will be collected and analyzed to identify biomarkers indicative of varying stress levels. Multimodal machine-learning models will be utilized to integrate, infer, and identify those biomarkers sufficient to characterize astronauts' cognitive-motor state at each stress level. This research will shed light on using multi-modal biometrics to monitor and assess astronaut operational performance that is under the influence of multiple spaceflight stressors. The experimental protocol can also be adapted as a battery of tests for training and selection of astronauts for missions that require multitasking and dealing with various stresses.

• Meet with PIs at weekly basis to discuss and make progress on the project,
• Collect and Analyze Data Developing VR task,
• Learning and Integrating new biosensors,

Required Skills:

• C#,
• C++,
• Unity,
• Matlab,

Desired Skills:

• Statistics,
• Psychology,
• Kinesiology / Physiology,

4

Tracking Global Atmospheric Moisture Transport

In the American West and specifically Wyoming, water resources are often limited so that precipitation changes can have detrimental effects on agriculture, industry, and ecosystems. Evaporated moisture can travel thousands of kilometers in the atmosphere before it precipitates. This project aims to understand global moisture transport, how upstream evaporation can be related to local precipitation, and how moisture transport is changing. Moisture tracking models use fields of evaporation, humidity, winds, and precipitation to track the movement of moisture in the atmosphere. They have become a popular method to study the sources of moisture that contribute to regional precipitation. In this project we will perform moisture-tracking simulations on a global scale, the results of which will be a global mapping from an evaporation field to a precipitation field. This new tool will have numerous scientific applications including 1) determining the extent to which precipitation changes from climate change are due to changing evaporation rates versus changing moisture transport, 2) testing how different patterns of evaporation relate to patterns of precipitation change, and 3) developing newfound understanding of global moisture transport. This project will be the first comprehensive mapping of global moisture transport and will extensively utilize the NCAR-Wyoming Supercomputing Center (NWSC). The selected student will have opportunities to learn about high-performance computing, geospatial data analysis, and atmospheric science, while performing cutting-edge research that is important to our understanding of the Earth and how it is changing. This work will contribute to the NASA-funded Global to Regional Origins of Water Stress (GROWS) project.

• Perform global moisture tracking simulations in python,
• Regularly meet with supervisor to discuss progress,
• Work with supervisor on interpretation of the moisture tracking model results,

Required Skills:

• Proficiency coding in python,
• Interest in atmospheric science (no experience required),
• Written and oral communication skill,

5

Computational assessment of the role of the chemical environment on protein form and function

The last five years have seen an explosion in the use of computation and AI-driven approaches to predict protein structure and interactions. While powerful, these new tools and approaches do not take into account the chemical environment that a protein exists or interaction in. To address this shortcoming we recently developed a user-friendly, high-throughput, fully customizable tool "OsmoFold" that allows one to predict the affect of small molecules on protein structure and interactions. Now, we are seeking a student to use and further refine this tool and associated approaches to better understand important questions in precision specialized medicine as well as protein-based pharmaceutical stabilization.

• Gathering/predicting test protein structures, running OsmoFold on test protein sturctures
• Analyzing OsmoFold output,
• Refining OsmoFold functionalities/development of computational pipelines associated with OsmoFold,
• Preparing reports on work/results generated,

Required Skills:

• Experience with Python is important as this is the language OsmoFold has been developed in,

Desired Skills:

• Some background in biology,
  • Do you know what a protein is?
  • Do you understand why a protein's structure is important for its function?

6

AI-Enabled Cultural Preservation Tools:

Developing Accessible Workflows for Transdisciplinary Scholars and Teams through Computing Research and Public Engagement

Many humanities and creative scholars work with fragmented, undocumented, or interpretive material, yet few computing tools are built with this context in mind. This project addresses that need through both technological development and community building. The Stage-Two AI tools will allow scholars to discover contextually relevant, multimodal content—even when source materials are anonymous, incomplete, or lack metadata. These tools will be invaluable in cultural preservation, visual anthropology, and public history. For example, a cultural historian might input a text-based description of a lost mural from a refugee community; the AI tool could then generate plausible visual reconstructions based on known regional styles and symbolic motifs, providing valuable material for public exhibits, digital storytelling, or collaborative and computing pedagogy. By combining AI search and generation, the project enables scholars to pursue exploratory, creative, and meaningful research, regardless of their technical background. It bridges traditional and computational methods while advancing the Computing for All mission at the University of Wyoming.

• Support and guide development of AI-enabled computing tools to help scholars:
  • Analyze and identify partial, historic, or speculative material via AI-assisted search tools,
  • Generate new cultural media assets,
• Prepare and migrate existing AI-enabled image and video generation workflows onto UW supercomputing systems,
• Assist in development of second-stage, AI-enabled tools to help scholars:
  • Search anonymous or fragmented media,
  • Generate new media based on anonymous, fragmented, and searched media,
• Develop workflows to scrape Web and online archive data,
• Acquire and maintain API keys and access,
• Develop original and AI-enabled media assets, UI prototypes, and test supercomputer systems (ARCC, NWSC),

Required Skills:

• Python

Desired Skills:

• Interest in aquiring or improving skills with:
  • Design Software,
  • Video Editing,
  • 3D Scanning,
  • Specimen Preparation Methods,
  • Web Development best practice skills for front-end UI,

7

Global patterns in tree-ring synchrony

In this project, we use wavelet-based techniques, derived from math and physics, to investigate how tree growth patterns rise and fall in tandem—what we refer to as “population synchrony”—across various tree species worldwide. We use tree ring data, which are natural records of how much an individual tree grew each year, to look back in time, sometimes over 1,000 years, to see how trees have responded to climate patterns, such as changes in temperature and precipitation. By comparing growth patterns from multiple locations, we can identify when and where trees grew in sync and what factors may have contributed to this phenomenon. Population synchrony has significant implications, as it highlights the risk of simultaneous declines, thereby making a species more vulnerable to extinction.

• Data analysis in R,
• Option to interpret and present findings in the form of a poster or talk at UWYO URID in the Spring,

Required Skills:

• Some prior experience in R,

Desired Skills:

• Interest in population ecology,

8

Patterns on the Path: Uncovering Trail Use Dynamics Across Time and Space in Teton County

This project involves analyzing trail counter data collected in Teton County to better understand spatial and temporal patterns of outdoor recreation. Trail counters provide timestamped records of pedestrian and cyclist activity at trailheads and along primary paths in the region. The student will apply spatial and temporal analysis techniques to examine usage trends by season, day of the week, and time of day, identify high-traffic trail segments, and explore potential correlations with weather, local events, and tourism dynamics. The project builds on insights and partnerships developed through our WORTH-supported research on tourism impacts and data-informed decision-making. It contributes to a broader effort to establish a cluster of education, research, and training in geocomputing for tourism, focusing on the application of computing tools to support public land management and community planning in Wyoming. The project also offers students practical experience in interpreting real-world geospatial and temporal data to inform infrastructure and policy decisions.

• Clean and preprocess raw trail counter datasets,
• Conduct exploratory spatial data analysis using Python,
• Create temporal plots and dashboards to summarize trail usage,
• Integrate and spatially align trail counter data with GIS datasets,
• Document findings and prepare visualizations for partner presentations,
• Participate in regular check-ins with the project mentor,

Required Skills:

• Basic proficiency in Python and use of Jupyter Notebooks,
• Interest in applying computing skills to real-world, data-driven problems,
• Willingness to engage with time-series and spatial data, with support and training provided,

Desired Skills:

• Experience with Python data libraries (e.g., pandas, geopandas) and basic machine learning workflows (e.g., scikit-learn),
• Prior exposure to time-series or spatial analysis techniques (e.g., trend detection, map-based visualization),
• Familiarity with version control using GitHub,

9

Tourism Through Local Eyes: A Sentiment Analysis of Fremont County Residents

This project utilizes a newly collected resident sentiment survey from Fremont County, Wyoming, concentrating on how tourism is viewed regarding its economic, social, and cultural impacts. The student will engage with both structured and open-ended survey responses, aiding in data cleaning, exploratory analysis, and visualizing sentiment patterns across various demographic and geographic groups.

In addition to standard statistical methods, the project will explore the application of machine learning and large language models (LLMs) to analyze free-text responses, extracting sentiment, identifying recurring themes, and highlighting areas of agreement or tension within the community. This work contributes to ongoing WORTH-supported efforts that combine public opinion, mobility, and economic data. It also supports our broader goal of building geocomputing capacity and applied AI training focused on rural planning and decision-making in Wyoming.

• Preprocess and clean structured and unstructured survey responses,
• Conduct exploratory data analysis and generate summary visualizations,
• Apply basic ML or LLM workflows to analyze sentiment in free-text responses,
• Identify key sentiment patterns by geography or demographic category,
• Assist in preparing briefings or visualization products for community stakeholders,

Required Skills:

• Basic proficiency in Python and use of Jupyter Notebooks,
• Interest in applying computing to community data and public issues,
• Willingness to learn natural language processing techniques and LLM workflows,

Desired Skills:

• Experience with Python data and visualization libraries (e.g., pandas, seaborn, matplotlib),
• Exposure to basic ML tools (e.g., scikit-learn, spaCy, or transformers),
• Interest in public sentiment, tourism policy, or rural planning,
• Familiarity with version control using GitHub

10

Identifying drivers of ecohydrological droughts and resilience in the Western U.S. using joint remote sensing and ground-based hydrological measurements

This research will integrate remote sensing and ground-based hydrological data in a scale-appropriate manner to account for and quantify ecohydrological fluxes that impact long-term vegetation health and resilience in the Western U.S. The Laramie Mountains and the Casper Aquifer at its mountain front, for which long-term monitoring data are being collected, define the pilot study area. For this area, the undergraduate researcher will compile, verify, and analyze co-located ground-based and remotely sensed precipitation, vegetation, soil moisture, and hydrological data as well as climate change parameters since 1980. Based on the joint data collection, drivers of ecohydrological fluxes during the growing season - snowmelt, streamflow, soil moisture, and groundwater that supply vegetation water use - will be identified using spatiotemporal Explainable AI. The relationships among the driver variables and the responses will be explored to determine the dominant influences on vegetation health & recovery under historical droughts & wetter than normal precipitation conditions since 1980. Based on results of the pilot study, future work will (1) build and verify a graph network model to explore spatiotemporal causation beyond the initially identified correlation; (2) using this causal model, future ecohydrological fluxes under a range of projected precipitation variability will be predicted for the pilot area; (3) based on results of the pilot study, hypothesize vegetation water use strategy that varies with ecohydrological fluxes and drivers for a range of mountain-to-basin ecosystems in the Western U.S. These hypotheses will form the foundation for an external proposal which will aim to test them for the larger regions in the West.

• Ground-based and remote sensing data collection and verification,
• Collaboration with a graduate student to analyze spatiotemporal correlation among feature input & response variables using a Random Forest model coupled with Explainable AI,
• Contribute to proposal preparation to an external funder,

Required Skills:

• course work or experience with remote sensing data download and interpretation

11

High-Order Physics-Guided Computational Simulation of Energy Recovery from Geothermal Reservoirs

Geothermal energy has significant potential as a sustainable energy source for the future. It is essential to accurately describe the geothermal reservoir’s physical, thermal, and chemical phenomena and adequately account for their complex interactions. The present project aims to develop a research program on the “High-Order Physics-Guided Computational Simulation of Energy Recovery from Geothermal Reservoirs,” which will significantly support graduate students in incorporating and advancing computing in their research relevant to this topic. With numerical geothermal reservoir models growing complexity and grid resolution, the simulators’ superior and optimal computational performance becomes essential for field development planning and decision-making. Developing this research program will help our trainees understand the application of computing in numerical geothermal reservoir modeling and simulation using industry-standard geothermal reservoir simulators. The proposed research program development aligns well with the University of Wyoming’s strategic education plan and opens the door for university-level and external national and international collaboration.

• Work on the project,

Required Skills:

• Prior knowledge on computer programming and geothermal reservoirs,

12

Spatio-temporal optimization of controlled environment agriculture

This project will explore the spatio-temporal dynamics of energy demand in controlled environment agriculture. Using publicly available datasets, the student will identify hotspots where current or future renewable energy generation, favorable weather, and local vegetable demand may support expanded CEA deployment.

• Geospatial data analysis,
• Report development,
• May include geospatial AI if consistent with participant interest and capabilities,

Required Skills:

• Familiarity with GIS and geospatial data,
• Strong written and verbal communication skills,
• Interest in and excitement for team science,

13

Characterizing the environmental impact of second-hand goods

This project is focused on using sales and distribution data in order to characterize the environmental impact of second-hand goods. As the circular economy becomes a more potent political force globally, it is increasingly important to understand the dynamics of and leverage points in the environmental footprint of second-hand products. Specifically, this project will use Goodwill data to identify key trends in the environmental footprint of sales and distribution of these products, including areas of opportunity and threats to overall environmental gains.

• Data analysis,
• Mapping,
• Report development,

Required Skills:

• Data analysis, ideally in Python,
• Strong written and verbal communication skills,
• Interest in team science,
• Interest or experience in environmental impact assessment