Accelerated Drug Discovery Intern: 2025
- San Jose, CA, USCambridge, MA, USAlbany, NY, US
- Software Engineering
- Intern
Accelerated Drug Discovery Intern: 2025
- San Jose, CA, USCambridge, MA, USAlbany, NY, US
- Software Engineering
- Intern
We are seeking summer intern candidates with proven interest and experience in developing advanced computational methods and pipelines, applied to accelerated drug discovery.
Your Role and Responsibilities
Successful candidates will work on creating, designing and applying new pipelines, tools and methods for AI foundational models or quantum computing used to accelerate the research and discovery of new targets and drug candidates. Specific projects may involve: building ML applications and UI tools (using Streamlit, React, Flask, or Django) for model interaction and visualization in a cloud environment leveraging Docker, OpenShift ,Git, Travis, and other tools; implementing deep learning models using PyTorch for drug discovery applications and performing distributed training to optimize model performance; efficiently managing, querying, and processing biological and chemical data using relational and key-value store databases.
Required Technical and Professional Expertise
- Applicants should be PhD & MS students pursuing graduate studies.
- An undergraduate degree or equivalent in computer science, electrical and computer engineering, computational biology, computational chemistry, molecular biology, molecular chemistry, or related fields.
- Experience with cloud tools for machine learning workflows (e.g., Docker, OpenShift), and SQL or NoSQL databases.
- Strong foundation in machine learning and deep learning with experience in PyTorch and distributed training.
- Strong problem-solving, and communication skills and a passion for learning and developing new technologies collaboratively.
Preferred Technical and Professional Expertise
- Basic understanding of chemistry, biology, molecular representations, and drug discovery workflows.
- Some experience with vector databases.
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