Undergraduate/Graduate Research Assistantship on AI and Machine Learning for Protein Modeling
Explore AI applications in protein modeling and bioinformatics. Develop machine learning solutions with Python and contribute to advancing biomedical research. Gain valuable experience working under an NSF Expand AI grant.
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Project Description
Project Overview
This project involves research in AI and machine learning under an NSF Expand AI grant, focusing on applications such as protein modeling, deep learning, and large language models. The work includes computational biology and bioinformatics, with goals to improve protein structure prediction and related tasks using advanced AI approaches.
What You Will Do
As a research assistant, you will develop machine learning pipelines using Python, work with deep learning models, and potentially contribute to projects involving large language models (LLMs). You will apply computational and statistical techniques to biological datasets and collaborate with team members on research publications and experiments.
Expected Outcomes
The project aims to enhance the accuracy of protein model quality estimation and homologous protein detection using deep learning frameworks. Results will contribute to improved understanding of protein structures, which is critical for advancements in biology and medicine.
Why This Matters
Improved protein modeling holds key benefits for drug design, disease understanding, and bioinformatics. Advancing AI methods in these areas accelerates scientific discovery and helps address biological challenges including viral diseases such as COVID-19.
Entry Requirements
How to Apply
Eligibility
Supervisor Profile
Dr. Sutanu Bhattacharya is an Assistant Professor of Computer Science at Auburn University at Montgomery. His research integrates computational biology, bioinformatics, and machine learning to develop advanced methods for protein structure modeling and analysis. His work features the development of deep learning approaches to improve protein model quality estimation and weak homolog detection, with significant contributions in the field including novel algorithms and applications to viral proteins.