Google DeepMind · London
Research Scientist, Material Intelligence
10/24/2025
Description
We are seeking an exceptional and highly motivated expert in computational materials science, with broad expertise simulating diverse material classes, to help drive our in-silico discovery efforts. This is a senior position with a unique role blending scientific leadership, hands-on modeling, strategic input, and mentorship. You will be instrumental in guiding the computational team, supervising junior researchers, and refining the critical in-silico feedback loop that is at the heart of our mission.
Key responsibilities:
- Computational Leadership & Supervision: Lead and mentor a team of computational materials scientists, guiding project roadmaps, fostering scientific growth, and ensuring high-quality research output.
- Modeling Strategy & Execution: Design and execute large-scale computational screening campaigns using DFT, molecular dynamics, and other simulation methods to predict novel materials with desired properties.
- Broad Materials Expertise: Apply deep physical and chemical intuition across diverse material classes to identify promising avenues for discovery.
- Method & Workflow Development: Review, integrate, and develop state-of-the-art computational tools and automated, high-throughput workflows on Google's large-scale compute infrastructure that can be tightly integrated with AI search methods.
- Data Integrity & Feedback Loop: Ensure the generation of high-quality, reproducible computational data. Play a key role in structuring and curating simulation databases to train next-generation AI models.
- Cross-functional Collaboration: Work closely with AI researchers and software engineers to translate AI-generated hypotheses into scalable simulation pipelines and to troubleshoot the simulation-to-reality gap.
- Reporting & Communication: Clearly and efficiently report on computational progress, new material predictions, and challenges to the wider Material Intelligence team and key stakeholders.
Qualifications
- Significant post-PhD experience in Computational Materials Science, Solid-State Chemistry, Condensed Matter Physics, or a related field.
- Proven track record of supervising and mentoring junior computational researchers, postdocs, or students.
- Broad knowledge across multiple material classes and their relevant properties (e.g., electronic, magnetic, optical, mechanical).
- Deep, recognized expertise in first-principles simulation methods for materials (e.g., DFT, DFPT, MD) and a strong understanding of their application and limitations.
- Extensive hands-on experience using computational packages like VASP, Quantum ESPRESSO, LAMMPS, or similar.
- Strong programming skills (e.g., Python) for workflow management, data analysis, and tool automation.
- Demonstrated ability to independently lead and manage complex computational research projects, from conception to data analysis and communication.
- Excellent teamwork and communication skills, with proven experience in interdisciplinary collaboration, especially bridging the gap between computational/theory and experimental groups.
- Experience in developing or applying machine learning models for materials property prediction (e.g., GNNs, ML-derived interatomic potentials).
- Expertise in high-throughput computational workflows and managing large-scale simulation campaigns on HPC or cloud infrastructure.
- A significant track record of high-impact research, reflected in publications, patents, or deployed technologies.
- Experience in strategic planning for a research group, including hiring and resource allocation.