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Machine Learning Research Assistant

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The Molecular Modeling Lab seeks motivated undergraduates to jump into cutting-edge AI research to accelerate early-stage drug discovery. Our lab researches novel computational methods for identifying small molecules that bind to and modulate the function of protein targets. We have many ideas on how to expedite this work with new AI methods, but we don’t have enough hands to implement everything.

As a research assistant, you will choose a project applying machine learning to small-molecule drug discovery. Working under the guidance of current graduate students in the lab, you will contribute to cutting-edge research in the field. The successful outcome of the research will earn you authorship in top journal publications and AI conferences.

Some possible projects include:
— Incorporating uncertainty into models predicting molecular activity
— Curating larger molecular datasets for advancing AI models
— Applying AI to speed up slow physics-based methods for predicting protein-ligand binding
— And many more!

Competitive students will possess a robust programming background; knowledge of chemistry/biology is a bonus. What we truly value is a profound curiosity about the world and the determination to make it a better place.

Interested students should reach out to

Faculty Advisor
Konstantin Popov
Research Supervisor
Michael Brocidiacono
Faculty Email:
Type of Position
Application Deadline