Click any of the Computational Science proposal summaries below for more information and to apply as a Rising Researcher.
Your deadline to apply is June 16.
Return to the complete list of available research opportunities.
Enhancing Rural Supply Chains with AlphaFold-Inspired Deep Learning (Soundar Kumara)
This project promises to substantially enhance rural supply chain resilience and efficiency. (learn more and apply – requires Penn State Login)
Accelerating AlphaFold3 for High-Throughput Protein Design (Soundar Kumara)
The proposed project targets major inference speed-ups for diffusion-based protein prediction models like Alphafold 3, Boltz and Chai -1 without significant loss of accuracy, enabling the evaluation of thousands of protein designs in silico. (Learn more and apply – requires Penn State login)
Dynamic numerical models of flank collapse and tsunami (Christelle Wauthier)
Numerical dynamic models using finite, discrete elements, or other numerical method to simulate collapse of volcanoes and tsunami propagation. (Learn more and apply)
Geodetic inversion and optimization using physics-based FEMs models and AI (Christelle Wauthier)
We will develop and apply AI and computational modeling methods to volcanic processes that will have broader impacts on forecasting. (Learn more and apply)
Using AI to learn and generate physically consistent and realistic landscape topography and fluvial river bathymetry (Xiaofeng Liu)
The objectives of the project are: (1) to investigate the inherent structural relationships between topography, river bathymetry, physiography, climate, precipitation, and river discharge. (2) to develop AI and ML models capable of generating synthetic, physically realistic landscape topography and river bathymetry. (Learn more and apply)
Transfer Learning for Predicting Local Atomic Order in Multi-Principal Element Alloys (Mia Jin)
This project aims to develop a machine learning framework that leverages transfer learning from binary alloy datasets to predict chemical short-range order (CSRO) in multi-principal element alloys (MPEAs), such as high-entropy alloys (HEAs), where data are scarce. (Learn more and apply)
Simulate and forecast magma propagation through fractures using extended finite elements (Christelle Wauthier)
We will develop and apply computationally intensive simulations methods to natural hazards processes and forecasting of eruption location. (Learn more and apply)
Machine-Learning Angle-Resolved Photoemission Spectroscopy under Tunable Magnetic Fields (Chaoxing Liu)
This project closely aligns with the objectives of CENSAI by leveraging machine learning approach to guide the design of cutting-edge experiments and drive foundational advances in quantum materials research. (Learn more and apply)
Advancing Air Pollution Exposure Assessment with Machine Learning Techniques (Xi Gong)
We will develop and apply data science and ML/AI methods to environmental health science to advance understanding, response, and mitigation of air pollution’s adverse health effects. (Learn more and apply)
AgriTwin: Real-Time Digital Twin Framework for Climate-Smart Farming (Dhananjay Singh)
AgriTwin is a scalable, AI-enabled Digital Twin platform designed to support emission sustainability in agriculture. (Learn more and apply)
Fire and climate change impacts in a tropical biodiversity hotspot (Rwenzori Mtns, Uganda): remote sensing to understand abrupt ecosystem change (Sarah Ivory)
This project will use remote sensing data (primarily Landsat, MODIS, ASTER) to reconstruct fire burned areas on a remote mountain. (Learn more and apply)
A tale of two [equatorial] mountains: state-space modeling of tropical plant communities from fossil data (Sarah Ivory)
In this project, we seek to use a community modeling approach, state-space modeling, to attribute climate drivers to ecosystem change on two equatorial African mountains in the past using fossil information. (Learn more and apply)
Development of an AI image classifier for detecting vulnerability of African ecosystems under changing climates using ancient data (Sarah Ivory)
In this project, we seek to develop a proof of concept for AI image classification of 4 African pollen taxa common in the fossil record. (Learn more and apply)
Using Artificial Intelligence (AI) to Understand Neural and Behavioral Variability (Xiao Liu)
We will develop and apply state-of-art AI models to understand brain functions. The project is also to understand the ANN from the perspective of the brain science. (Learn more and apply)
Development of a Web-Based Platform for Structured CryoEM Data Collection and Metadata Management (Jean-Paul Armache)
This project aims to develop a secure, user-friendly web-based platform to collect, store, and manage cryoEM data collection parameters in a complementary automated and manual approach. (Learn more and apply)
Reduced order modeling for supersonic and hypersonic aerodynamic flows via probabilistic machine learning (Ashwin Renganathan)
We will develop probabilistic AI/ML methods to reduce, interpret, and learn data. This project will include both large-scale data generation by running finite-volume based multiphysics codes on Roar Collab, as well as developing AI/ML methods on that data with GPU acceleration. (Learn more and apply)
Computational Mapping of Alternative Dispute Resolution Institutions (Cyanne Loyle)
By integrating social science expertise with computational tools, the project will provide foundational data for the social science community in the area of conflict management and peace building. (Learn more and apply)
Develop machine learning models to study cell-type-specific aging using single-cell methylation data in the Uzun Lab (Yasin Uzun)
Our goal is to develop a deep learning-based framework to predict cell-type-specific epigenetic age using single-cell methylation data. (Learn more and apply)
Development of standardized file format to maximize data shareability across disciplines (Jean-Paul Armache)
In this proposal, we intend on establishing a standardized file format designed for data sharing in reviews, or as project summaries. (Learn more and apply)
Building representative karst morphologies to model saltwater intrusion in South Florida (Rachel Housego)
In this project the junior researcher will create numerical model domains that incorporate representative karst morphologies of South Florida to evaluate saltwater intrusion risk. (Learn more and apply)
Integrating marine geochemistry, physical dynamics, and volcanology in the geological record: an Oceanic Anoxic Event 2 case study (Isabel Fendley)
For this project, the junior researcher will develop a computational framework to integrate geochemical models for each of the key proxies (Hg, Os, and Sr). (Learn more and apply)
Improving economic outcomes via AI-powered bank monitoring and risk management (Nonna Sorokina)
By integrating expertise in finance, economics, regulatory policy, and artificial intelligence, the initiative aims to build an AI-powered monitoring framework for banking risk management—particularly vital in today’s volatile interest rate environment. (Learn more and apply)
De-risking the commercialization of advanced nuclear reactors through innovative financing vehicles (Nonna Sorokina)
By developing innovative financial mechanisms—including pooled investment models, securitization strategies, and CDS-like instruments—this research synthesizes technical reactor design considerations with sophisticated computational modeling of risk and return. (Learn more and apply)
Your next-door neighbor, nuclear reactor: real estate and societal readiness (Nonna Sorokina)
By examining real estate dynamics around nuclear power plants and incorporating novel measures of public sentiment and societal readiness, the research brings together expertise from economics, urban planning, nuclear engineering, and computational social science. (Learn more and apply)
Digital Thebes: A Comprehensive Database for Egyptian Antiquities Education and Research (Ziting Wang)
The “Digital Thebes” project aims to create a comprehensive, interactive website and database of digital educational resources focused on ancient Egyptian archaeological sites, particularly nonroyal tombs in the Theban cemetery in the New Kingdom period. (Learn more and apply)
Resource Request for HPC Deployment Automation for AI/HPC systems (Gary Skouson)
The ICDS Systems Engineering team is looking for assistance to develop and/or improve system deployment automation for the ICDS AI and HPC systems. (Learn more and apply)
Resource Request for Junior Researchers (Systems Engineering) support for cluster account lifecycle management (Ross Mickens)
Assist ICDS technical staff with developing a systems approach to account lifecycle management that does more than meet the needs of researchers. (Learn more and apply)
Developing Workforce-Informed Digital Twins for Smart Redevelopment Site Classification (Yuqing Hu)
This project addresses that gap by developing a graph-based digital twin framework to classify and prioritize redevelopment sites based on workforce and infrastructure readiness. (Learn more and apply)
Privacy-Preserving Linear Regression and Synthetic Data for Reproducible Social Science Research (Aleksandra Slavkovic)
This project aims to develop a novel method for DP linear regression that enables valid statistical inference and supports synthetic data generation. (Learn more and apply)
Volcanic (LIP) gas fluxes in geological history using geochemical models (Isabel Fendley)
The key goals of this project are a) to finalize the framework for data-model comparison (e.g., evaluate parameter choices, test various metrics for statistically comparing records), b) optimize the Earth system and Hg cycle code for computational efficiency and the same for parameter sampling in the Bayesian framework, and c) set up and run the model inversion on the Roar Collab Cluster. (Learn more and apply)
Linking Multidimensional Sleep Health to Cognitive Function in Older Adults Using Machine Learning (Sayed Reza)
This project will evaluate the relationship between sleep health and cognitive function in older adults by leveraging wearable device time series data and applying interpretable AI/ML techniques. (Learn more and apply)
Identify the causes of the signal-to-noise paradox in the North Atlantic Oscillation (Laifang Li)
The project will utilize advanced data analysis technique to address one of the most challenging Earth system predictability issues in the climate community. (Learn more and apply)
Fitting accretion disk models to optical spectra of supermassive black holes: faster exploration of parameter degeneracies with nested sampling (Charlotte Ward)
Develop a faster version of a pre-existing Python and Fortran package that can predict a spectrum based on a given set of disk parameters in python/jax, and demonstrate efficient nested sampling for model fitting. (Learn more and apply)
Address the impacts of changing ocean circulation on US hydroclimate (Laifang Li)
This project aims to answer climate system questions by synergistically using global climate model out from the North Atlantic Hosing Model Intercomparison Project and the numerical downscaling with regional climate models. (Learn more and apply)
Enhancing the Roar User Experience (Wayne Figurelle)
This project gives the student the ability to impact researchers by making them more efficient while developing some tools that allow the student to learn prediction. (Learn more and apply)
Development of High-fidelity and Reduced-order Models for Thermal Runaway (Ashish Kumar)
The proposal draws faculty members from two colleges to research thermal runaway in large-format batteries. The results of this study will make a substantial contribution to the existing knowledge regarding the improvement of large-format battery safety as their applications continue to grow. (Learn more and apply)
Assessment of Geological CO Storage and Geothermal Resources in the Appalachian Basin and Globally (John Wang)
This interdisciplinary research project aims to assess the viability and sustainability of geological CO2 storage and geothermal energy resources in the Appalachian Basin and globally, with a strong focus on applying data science methods to climate and energy challenges. (Learn more and apply)
Zentropy-Based Insights into Short-Range Order and Mechanical Properties in High Entropy Alloys (Shunli Shang)
In this seed project, we aim to predict both chemical and magnetic ordering in the model alloy VCoNi using the Zentropy theory, and to investigate how these orders impact mechanical properties such as tensile and shear strengthen. (Learn more and apply)
Protein Misfolding, Mutations and the Emergence of Disease Phenotypes (Hyebin Song)
This project aims to identify and rank proteins containing structural motifs known as “non-covalent lasso entanglements” and assess their association with disease phenotypes. (Learn more and apply)
Topological Data Analysis for the Quantification of Prostate Cancer Heterogeneity (Justin D Silverman)
This project will develop computational tools to quantify the 3D morphology of prostate cancer glands, supplementing and potentially improving upon traditional tumor grading systems. (Learn more and apply)
An improved pipeline to detect astrophysical transients in Atacama Cosmology Telescope time-resolved survey data (Charlotte Ward)
In this project, the junior researcher will build an improved pipeline to extract light curves of low flux sources from multi-epoch imaging from the Atacama Cosmology Telescope. (Learn more and apply)
Mechanisms and predictability of subsurface marine heatwaves along the continental shelf of eastern US (Laifang Li)
In this project, the PI propose to investigate manifestation of the subsurface marine heatwaves in the past 30 years, further elucidate the initialization mechanisms of subsurface heatwaves, and assess their predictability using the high-resolution (~1 km) regional ocean modeling system (ROMS). (Learn more and apply)
Deciphering systemic biological networks through AI-driven multi-omic integration (Gustavo Nader)
We will integrate publicly available multi-organ, multi-omic dataset to investigate the molecular hierarchies that establish organ cross-talk and optimal organismal physiological adaptations and function. (Learn more and apply)
Analyzing Human and Social Dynamics Through Social Sensing (Xi Gong)
This project aims to expand the current study using social sensing for understanding spatial social networks and public perspectives on controversial social topics, also exploring dealing with the challenges inherited in social sensing research. (Learn more and apply)
Enhancing Road Safety Through Real-Time AI-Powered Drowsiness Detection and Alert system Using EEG Eye-Blink Artifacts (Daniel Otchere)
This proposal seeks to develop an innovative AI-powered system for detecting driver drowsiness through real-time analysis of EEG eye-blink artifacts. (Learn more and apply)
Application of Transformer-Based Machine Learning Models to Whole Organism Computational Phenomics (Keith Cheng)
To enable the first 3-dimensional whole-organism phenotyping that encompasses all cell types and organ systems, we propose to develop and optimize Transformer-based machine learning (ML) models capable of automatically segmenting and labeling regions of interest from high-resolution 3D micro-CT scans at unprecedented resolutions. (Learn more and apply)
AI-Supported Cyber Safety Curriculum for Youth: Design, Development, and Evaluation (Ellen Wenting Zou)
Project objectives include the design and prototyping of four interactive curriculum modules, the development of AI-powered learning scenarios, and initial user testing with middle and high school students to refine both content and interface. (Learn more and apply)
Building Digital Twins of Personalized Models for Alzheimer’s Disease Prevention and Treatment (Zi-Kui Liu)
The proposed project aims to develop a Zentropy-Enhanced Neural Network (ZENN) that learns the configurations, total energy, and entropy of brain states using data related to Alzheimer’s disease (AD). (Learn more and apply)
Computational Perspectives for Quantum Phases of Matter (Zhen Bi)
By analyzing how the mixing time scales with system size, temperature, and Hamiltonian parameters, this project will develop a computational perspective for comparing quantum phases and for pinpointing the dynamical signatures of phase transitions. (Learn more and apply)
Advanced Deblurring of Electron Beam Induced Motion for High-Resolution CryoEM 3D Reconstructions using Electron Event Data (Wen Jiang)
The primary goal of this project is to develop and implement a novel deblurring methodology for cryo-EM data that leverages the high temporal and spatial resolution of electron event recordings. (Learn more and apply)
Predict Arctic Sea Ice Variability from Atmospheric River Activities and the Time of Arrival of Ice-free Arctic (Laifang Li)
In this project we propose to apply deep-learning models (e.g., convolutional neural networks; CNN) to predict Arctic sea ice variability based on the life cycle of ARs. (Learn more and apply)
Learning on the Edge With Hyperdimensional Computing (Vasant Honavar)
This project aims to develop and evaluate lightweight, HD computing based machine learning framework for learning on the edge, that ls, learning predictive models from data being acquired by edge devices. The resulting methods will also help significantly reduce the carbon footprint of machine learning. (Learn more and apply)
Federated estimation of causal effects from observational data (Vasant Honavar)
A long term goal of this project is to develop robust federated algorithms for causal effect estimation for a broad range of applications in healthcare, education, public policy, etc. where it is generally neither feasible nor desirable to aggregate data collected by independent entities into a centralized repository. (Learn more and apply)
Efficient Adaptation of Trained Models When Utilities of Model Predictions Change (Vasant Honavar)
The long-term goal of this project is to develop methods for efficient adaptation of predictive models trained using machine learning when the utilities of model predictions change, with practical applications across a broad range of real-world applications. (Learn more and apply)
Machine Learning for Health Risk Prediction from Longitudinal Health Data (Vasant Honavar)
The long term goal of this project is to establish a unified, modular framework for temporal clinical modeling that is generalizable across datasets, interpretable for clinicians, and adaptable to other domains of risk prediction. (Learn more and apply)
Spinal Fatigue Prediction in High-G Environments Using Human Digital Twins (Reuben Kraft)
This project develops a digital twin framework to evaluate spinal fatigue in pilots subjected to high G acceleration. (Learn more and apply)
Using Artificial Intelligence (AI) to Understand Neural and Behavioral Variability (Xiao Liu)
We will develop and apply state-of-art AI models to understand brain functions. The project is also to understand the ANN from the perspective of the brain science. (Learn more and apply)
Development of a Digital Twin Model for Stirred Milling Process by Integrating Machine Learning Models and Discrete Element Method Simulations (Olumide Ogunmodimu)
This project aims to contribute to the evolution of digital twin applications by integrating a combined approach of machine learning models, including Support Vector Machines (SVM), Convolutional and Graph Neural Networks (CCNN, GNN), Physics-Informed Neural Networks (PINN), and Discrete Element Method (DEM) simulations. (Learn more and apply)