Research Scientist, Material Intelligence, 12-Month Fixed-Term Contract

Snapshot

Science is at the heart of everything we do at Google DeepMind. From the beginning, we took inspiration from science to build better algorithms, and now, we want to use our toolkit to accelerate scientific discovery. By bringing together specialists with backgrounds in machine learning, computer science, physics, chemistry, biology and more, we’re optimistic that we can build new methods that will push the boundaries of what is possible and help solve the biggest problems facing humanity.

This role is a 12-month Fixed Term Contract, based in London. 

About Us

Google DeepMind (GDM) is pursuing a ground-breaking research program in materials, aiming to accelerate the discovery of new functional materials by combining the predictive power of artificial intelligence (AI) and computational simulation with automated experimentation. The team is establishing experimental capacity to create a closed-loop, AI-driven discovery engine. Computational simulation is critical for grounding the AI and providing quick in silico feedback before materials are sent off to the lab for experimental validation.

The Role

We are seeking a highly motivated computational materials scientist to join our in-silico discovery efforts on a 12-Month Fixed Term Contract. We are hiring for multiple positions and are looking for candidates with deep expertise in simulating functional materials in areas such as superconductors, semiconductors, magnets and energy materials. This role is focused on hands-on modeling and in-depth analysis to help discover next-generation materials. You will be a key contributor to our computational team, designing and running advanced simulations and collaborating closely with senior researchers and AI specialists to refine the critical in-silico feedback loop that is at the heart of our mission.

Key responsibilities:

• Hands-on Simulation & Analysis: Execute and analyze advanced computational simulations (e.g., DFT, DFPT, MD, lattice models) with a strong focus on technologically  important properties of materials such as electronic, magnetic or optical properties etc.
• Discovery Campaigns: Contribute to the design and execution of computational screening campaigns to identify and optimize novel materials with desired properties.
• Workflow Execution: Utilize and help refine state-of-the-art computational tools and automated, high-throughput workflows on our large-scale compute infrastructure.
• Data Generation & Integrity: Ensure the generation of high-quality, reproducible computational data from your simulations. Contribute to structuring and curating simulation databases to train next-generation AI models.
• Cross-functional Collaboration: Work closely with AI researchers and software engineers to run scalable simulation pipelines based on AI-generated hypotheses and to help troubleshoot the simulation-to-reality gap.
• Reporting & Communication: Clearly and efficiently report on computational progress, new material predictions, and challenges to the computational team and wider Material Intelligence group.

About You

In order to set you up for success as a Research Scientist at Google DeepMind,  we look for the following skills and experience:

• A PhD in Computational Materials Science, Solid-State Chemistry, Condensed Matter Physics, or a related field.
• Specialist knowledge and research experience in an area of technologically important materials.
• Strong technical expertise in first-principles simulation methods.
• Hands-on experience using computational packages like VASP, Quantum ESPRESSO, or similar.
• Strong programming skills (e.g., Python) for workflow management, data analysis, and tool automation.
• Demonstrated ability to manage and execute computational research tasks effectively, from simulation setup to data analysis and communication.
• Excellent teamwork and communication skills, with a desire to work in a fast-paced, interdisciplinary collaborative environment.

In addition, the following would be an advantage: 

• Experience in developing or applying machine learning models for materials property prediction.
• Experience with high-throughput computational workflows and running simulations on HPC or cloud infrastructure.
• Experience with molecular dynamics (MD) packages like LAMMPS, especially using ML-derived interatomic potentials.
• A track record of research published in peer-reviewed journals.

Applications close: 6th November 

 

 

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.