Sustainability Research Intern – IBM

Location: San Jose, California or Yorktown Heights, New York

Overview:

We are seeking candidates with proven interest and experience in accelerating the discovery of sustainable materials. This could involve developing machine learning methodologies/pipelines including explainable AI and visualization methods, large-scale simulation coupled with active learning and/or machine learning potentials, exploration of future experimental lab technologies, or development of the mathematics of machine learning, AI, or quantum computing. Specific materials topics of interest include energy storage, semiconductors, metal-organic frameworks for carbon capture, and design of replacements for PFAS materials. You will work in close collaboration with other researchers and engineers and are expected to make substantial contributions to shared code bases, presentations at top conferences in the fields of AI and computational materials science (e.g., AAAI, NeuRIPS, ACS, MRS), and publications in high impact journals. Demonstrated communication skills are essential.

Required Technical and Professional Expertise: 

• Programming or data science experience with Python
•  Current pursuit of a graduate degree or equivalent in chemistry, materials science, chemical engineering, cheminformatics, materials informatics, computer science, artificial intelligence, or a related field.
• An undergraduate degree or equivalent.
• You would need to work in one of the following locations: Yorktown Heights, NY or San Jose, CA. the World is Our Laboratory: No matter where discovery takes place, IBM researchers push the boundaries of science, technology and business to make the World work better. IBM Research is a global community of forward-thinkers working towards a common goal: progress

Preferred Technical and Professional Expertise:

• General experience with materials science or chemistry
• Core tools for machine learning and modeling, including generative modeling
• Theoretical foundations and/or deep technical knowledge of the above