Senior Applied Scientist in Materials - Microsoft Research AI for Science

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Microsoft Research AI for Science is seeking a talented applied scientist 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. You can learn more about our AI emulator MatterSim and generator MatterGen in our blog.

This role is an exceptional opportunity to bring our foundational AI capabilities toward real-world impact. You will work closely with experimental partners and apply our foundational AI models toward solving materials design problems. You will work with a highly collaborative, interdisciplinary, and diverse global team of researchers and engineers to bridge the gap between computation and experiments.

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 the world.

This post will be open until the position is filled.

Qualifications:

Required:

• PhD in experimental or computational materials science, condensed matter physics, machine learning, or related area, or comparable industry experience.

Experience:

• Experience in applying machine learning models to solve real-world problems in materials science.
• Proficiency in collaborative code development in Python on shared codebases.
• Publication track record in relevant academic journals (npj computational materials, Nature Materials, PRB, PRL, etc.).
• Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds.

Preferred:

• Hands-on experience in solid state synthesis and characterization techniques.
• Experience in deeply collaborating with experimental groups and applying computational materials design approaches to discover novel materials.
• Experience in training and fine-tuning machine learning force fields or generative models.

#Research #AI for Science

• Work closely with experimental partners and translate foundational AI capabilities towards real-world impact.
• Learn to interpret experimental data and translate them to actionable insights for model development.
• Apply machine learning models, including generative models and machine learning force fields, to speed up the discovery of novel materials.
• Generate domain-specific datasets and fine-tune machine learning models.
• Collaborate with experimental partners to validate computationally designed materials candidates in laboratories.
• Prepare technical papers, presentations, and open-source releases of research code.