Introduction
Accelerated Discovery at IBM Research is seeking candidates who will contribute to foundational research topics in machine learning and AI. Start and end dates for this Internship are during Summer 2025 (3 months) at IBM Research Almaden in San Jose California. At IBM Research, we invent things that matter to the world. Today, we are pioneering the most promising and disruptive technologies that will transform industries and society, including the future of Hybrid Cloud and AI. And looking to the horizon, we are placing big bets on a pipeline of ambitious exploratory science breakthroughs which will be foundational for the future of computing. With more than 3,000 researchers in 12 labs located across six continents, IBM Research is one of the world's largest and most influential corporate research labs.
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Your Role and Responsibilities
This is for a 2025 summer internship with the following start dates: May - August or June - September for quarter system schools.
IBM research interns are expected to develop innovative ideas and concepts (theoretical or experimental) while performing advanced scientific or engineering tasks resulting in highly complex, original and creative scientific or engineering achievements.
As research scientist intern in Accelerated Discovery, you will work closely with an assigned mentor on a project using AI and machine learning to solve problems in biochemistry, biophysics, and/or material sciences. This project may involve implementing novel AI architectures, and/or defining and running experiments using existing AI architectures. You will also be expected to write a research report or paper on the research done during your internship and present your work at internal research meetings and external workshops and/or conferences.
Required Technical and Professional Expertise
- Applicants should be PhD & MS students pursuing graduate studies.
- Strong programming skills and the ability to program in Python to implement methods for AI.
- Experience conducting machine learning experiments and interpreting the results.
- Knowledge of high-performance computing environments.
- Machine learning engineering skills, including building training pipelines and evaluating models using toolkits such as PyTorch, TensorFlow, and scikit-learn.
- Strong communication skills.
Preferred Technical and Professional Expertise
- Experience solving analytical problems using rigorous and quantitative approaches.
- Design, validation, and characterization of algorithms and/or systems.
- Knowledge and understanding of molecular dynamics.
- A track record of co-authoring technical reports and publications.
- Experience using Git and GitHub.
- Experience in doing software development as part of a multidisciplinary research and development team.