IBM Research takes responsibility for technology and its role in society. Working in IBM Research means you'll join a team who invent what's next in computing, always choosing the big, urgent and mind-bending work that endures and shapes generations. Our passion for discovery, and excitement for defining the future of tech, is what builds our strong culture around solving problems for clients and seeing the real world impact that you can make.
IBM's product and technology landscape includes Research, Software, and Infrastructure. Entering this domain positions you at the heart of IBM, where growth and innovation thrive.
As a summer intern, you will contribute to the design and development of large-scale AI models and algorithms for applications in the domain of biomedical research. Your work will focus on building and evaluating research prototypes that advance AI methods for scientific applications grounded in the principles of physics, chemistry and biological sciences. Our team collaborates actively with biomedical researchers in healthcare and pharmaceutical industries to develop AI agentic workflows and foundation models tailored towards their fields.
- Strong foundation in mathematics, physics and chemistry
- Experience with algorithms development and scientific computing
- Experience with AI/ML model design and training
- Proficiency in Python and modern deep learning frameworks (e.g., PyTorch)
- Collaborative mindset to work within an interdisciplinary team and with agile coding principles
- A drive to produce innovative, publishable research
- Current enrollment in Ph.D. program with a track record of research in referenced areas
- Experience working with large, complex biomedical datasets and high-performance computing environments
- Background in physics-based modeling, with a preferred focus on chemical and biophysical simulation
- Background in developing and implementing novel AI methods
- The ability to reason about complex biomedical problems
- Background knowledge applying quantum mechanics in the field of physics-based modeling