Microsoft Research AI for Science is seeking a talented machine learning researcher to join our mission of accelerating scientific discovery through AI for an internship. In the materials team, we are building next generation foundational AI capabilities to accelerate the design of novel materials with industrial impact. You can learn more about our AI emulator MatterSim and generator MatterGen in our blog.

This internship is an exceptional opportunity to contribute to our ambitious research agenda by developing efficient and expressive machine learning models for materials. You will work with a highly collaborative, interdisciplinary and diverse team of researchers, engineers and scientists to develop and implement the next generation of machine learning models for materials design.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more, and we’re dedicated to this mission across every aspect of our company. Our culture is centered on embracing a growth mindset and encouraging teams and leaders to bring their best each day. Join us and help shape the future of materials design.

• Location: Berlin, Germany,
• Lenght: 16 weeks

• 
  Contribute to and drive an ambitious, high-impact, research agenda on machine learning for materials.

• Develop efficient and expressive machine learning models that address fundamental materials science problems.

• Work with domain experts to develop realistic machine learning metrics and benchmarks.

• Prepare technical papers and presentations.

Required/Minimum Qualifications:

• Currently enrolled in a Master’s degree or PhD program in computer science, machine learning, computational materials science or related area.

• Track record of publications at top-tier conferences or journals (e.g., NeurIPS, ICML, ICLR, Nature/Science or relevant sub-journals).

• Strong coding ability and proficiency in collaborative code development.

• Ability to quickly iterate between ideation, implementation and evaluation of new research ideas.

• Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds.