Work on the development and implementation of a novel in house peptide and mini binder design platform
Integrate enhanced sampling such as Monte Carlo tree search
Integrate various machine learning property predictors into the pipeline
Benchmark Novo Bind on diverse systems and improve the pipeline scope
Implement reinforcement learning to bias the generation of designs
Learn about state of the art structure predictors and metric, computational sequence-based binder design, reinforcement learning, drug design with computational chemistry

Current student enrolled at an accredited college or university pursuing at least a Doctorate degree
In progress doctorate degree in Computer Science, Machine Learning, Artificial Intelligence, Computational Chemistry, or a related discipline preferred
Strong academic record with a preferred cumulative GPA of 3.0 or higher
Conscientious self-starter with good organizational skills, project management skills and attention to detail
Ability to balance multiple projects and priorities, must be able to multi-task
Strong interpersonal communication and ability to collaborate with teams
Demonstrated personal initiative, self-motivation, flexibility, adaptability and willingness to learn
Proficient in Microsoft Office Tools including Word, Excel, PowerPoint, etc.

Internship – Molecular AI

Key skills