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AI for Science Residency - Machine Learning Resident

Microsoft

AI for Science Residency - Machine Learning Resident

full-timePosted: Aug 19, 2026Updated: Aug 27, 2026Berlin, BE, DE

Job Description

OverviewMicrosoft Research AI for Science is seeking a talented machine learning researcher to join our mission of accelerating scientific discovery through AI. 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 2-year residency position 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 centred 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. ResponsibilitiesDrive 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. Contribute to building large-scale infrastructure for data generation, model training and inference.Keep up-to-date with latest developments in the field. QualificationsRequired/Minimum Qualifications: PhD in computer science, machine learning, computational materials science, physics, 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. Preferred/Additional Qualifications: Experience working on generative models. Experience working on (materials) science problems. Experience with agent-driven research and code development. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Locations

  • Berlin, BE, DE
  • Cambridge, England, GB
  • Amsterdam, NH, NL

Skills Required

  • collaborative code developmentintermediate
  • agent-driven researchintermediate

Required Qualifications

  • PhD in computer science, machine learning, computational materials science, physics, or related area. (degree in computer science)
  • PhD in computer science, machine learning, computational materials science, physics, or related area. (degree in computer science)
  • Track record of publications at top-tier conferences or journals (e.g., NeurIPS, ICML, ICLR, Nature/Science or relevant sub-journals). (experience)
  • Track record of publications at top-tier conferences or journals (e.g., NeurIPS, ICML, ICLR, Nature/Science or relevant sub-journals). (experience)
  • Strong coding ability and proficiency in collaborative code development. (experience)
  • Strong coding ability and proficiency in collaborative code development. (experience)
  • Ability to quickly iterate between ideation, implementation and evaluation of new research ideas. (experience)
  • Ability to quickly iterate between ideation, implementation and evaluation of new research ideas. (experience)
  • Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds. (experience)
  • Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds. (experience)

Preferred Qualifications

  • Experience working on generative models. (experience)
  • Experience working on generative models. (experience)
  • Experience working on (materials) science problems. (experience)
  • Experience working on (materials) science problems. (experience)
  • Experience with agent-driven research and code development. (experience)
  • Experience with agent-driven research and code development. (experience)
  • This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. (experience)

Responsibilities

  • 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.
  • Contribute to building large-scale infrastructure for data generation, model training and inference.
  • Keep up-to-date with latest developments in the field.

Benefits

  • general: Flexibility: Balance what matters—your work, your life, and your team—through trust, autonomy, and shared accountability
  • general: Growth: Stretch your skills, expand your impact, and grow with support that meets you where you are
  • general: Wellbeing: Support for your body, mind, and financial future—so you can stay energized and do your best work
  • general: Community PCS: Find your people, build your network, and feel supported every step of the way

Travel Requirements

Less than 25%

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