OPS Research Assistant - AI-driven Drug Discovery

The OPS Research Assistant in AI-driven Drug Discovery will work in the lab of Dr. Yanjun Li in the College of Pharmacy's Medicinal Chemistry department.

Job Description:

• Be an active member of a scientific group that develops and prototypes novel AI algorithms for impactful drug discovery problems involving sequence and structure modeling for protein, RNA, or small molecule.
• Apply computational techniques, data analysis, and visualization methods for ongoing studies.
• Train and validate large deep learning models at scale.
• Develop optimized data pipelines to fuel deep learning models, and maximize the value of public and proprietary data assets.
• Collaborate closely with PI, research assistants, computational biologists, experimental biologists, and chemists on research projects and scientific papers.

Starting at $15/hour; Commensurate with education and experience

Bachelor's or Master's degree in computer science, electronic engineering, bioinformatics, computational biology, computational chemistry, statistics, data science, or related disciplines.

• Interests in AI-driven drug discovery, deep learning research, and self-motivated.
• Solid Math/Statistics and coding skills in Python/R, PyTorch, and TensorFlow.
• Demonstrated research experience in the deep learning and computational biology field.
• Good written and oral communication skills. Proactive, perseverant, and eager to learn.
• Ability to manage time efficiently and meet deadlines.

To be considered, you must upload your cover letter, resume, and list of three professional references.

As a part of our review for research and research support positions, we look for a full CV which includes all professional appointments/engagements, all post-secondary education, and all publications from post-secondary education and all respective dates. Please be sure to not use acronyms in your CV. This CV should include names of entities associated with any projects.

This position is in-person and is not eligible for remote work.

Review of applications will begin Jan. 25, 2025, and continue until the position is filled.

Application must be submitted by 11:55 p.m. (ET) of the posting end date.