Cambridge Residency Programme

A materials science research position at the interface of ML accelerated materials discovery and synthesis, focusing on simulations of materials and interfaces, for AI Infrastructure.

This is a 24-month fixed-term post-doctoral Residency position within the Future AI Infrastructure research group at Microsoft Research Cambridge (UK), as part of our “Materials for the Cloud” project to improve the cost, performance or sustainability of future AI cloud infrastructure. This is an extraordinary opportunity to work at the forefront of materials science in collaboration with researchers from Microsoft Research (AI for Science) to utilise foundational models for predicting new materials with exciting properties. In addition to this, you will benefit from working closely with experimental researchers and in-house materials synthesis to further refine these models using lab generated data.

We are looking for a researcher with skills in computational materials science, DFT simulations for materials, and molecular dynamics, to design and refine workflows for complex materials such as amorphous-crystalline coexistence, solid-solid interfaces, and thin-films.

The role will focus on developing and improving simulation workflows for amorphous and crystalline materials, including using the MatterSim model and high-throughput data generation pipelines. You will work closely with researchers and experimentalists in materials science, AI, and simulation, contributing to advancing simulation methods for complex materials, including the development of new approaches for modelling atomic interactions, generating and analysing amorphous and interfacial structures, and implementing high-throughput workflows. You will contribute to building robust datasets, improving model accuracy, and enabling systematic exploration of material properties to support innovative materials design and

Required:

• PhD in Materials Science, Solid-state Physics, Computational Chemistry, or a related field, or equivalent training and experience in research.
• Strong background in theoretical and computational material science.
• Experience with DFT methods and workflows.
• Strong software development skills (e.g. in Python)
• Excellent written and verbal communication skills.
• Ability to work independently and as part of a cross-disciplinary team.
• Experience with molecular dynamics simulation.

Preferred Qualifications:

• Familiarity with phase-change materials, thin-film materials, or device physics.
• Experience with solid-solid interface simulations.
• Experience with high-throughput simulation workflows.
• Familiarity with DFT methods for optical properties.
• Experience with amorphous phase simulations and analysis.
• Understanding of free energy simulation methods such as meta-dynamics.
• Experience working with experimental collaborators and with automated laboratory environment.
• Demonstrated communication skills through impactful academic publications.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

• Development of advanced simulation workflows for complex materials and interfaces.
• Develop and refine computational models for atomic-scale interactions and material properties.
• Contribute to the creation and curation of datasets supporting model development and validation.
• Collaborate within a multidisciplinary team—including experts in machine learning, storage materials, and experimental science—to enhance methods for analysing and predicting material behaviour.
• Support the implementation of scalable, automated approaches for materials discovery and characterisation.
• Communicate research progress and contribute to scientific publications and presentations.