Click any of the AI proposal summaries below for more information and to apply as a Junior Researcher. Your deadline to apply is June 16.
Return to the complete list of available research opportunities.
Enhancing Satellite Deformation Measurements using Deep Learning (Christelle Wauthier)
Using AI deep learning approaches to maximize the output of satellite deformation measurements using realistic atmospheric models is still in its infancy. (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)
Forecasting volcanic eruptions using data fusion (Christelle Wauthier)
We will develop and apply data sciences and AI methods to volcanic hazards processes and hope to improve eruption forecasting globally. (Learn more and apply)
Using AI to learn and generate physically consistent and realistic landscape topography and fluvial river bathymetry (Xiaofang 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)
Normalizing flows for Bayesian Model Comparison: Detecting Extrasolar Planets (Eric Ford)
This project compares the robustness and efficiency of different computational methods for performing Bayesian uncertainty quantification and model comparison to improve the sensitivity and robustness of surveys to discover and characterize low-mass planets. (Learn more and apply)