Semiconductor Material Science Research Scientist, DeepMind New

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Execute and analyze advanced computational simulations (e.g., DFT, DFPT, MD) with a strong focus on predicting key properties for semiconductors, such as band gaps, defect levels, leakage currents, dielectric constants, and interfacial properties.

Apply deep physical and chemical intuition to problems in semiconductor materials discovery, particularly understanding structure-property relationships at the atomic scale and at interfaces with semiconductors.

Bridge the gap between theory and reality by using computational tools to identify semiconductor materials and working with experimentalists to synthesize them in the lab.

Minimum qualifications:

PhD in Computational Materials Science, Solid-State Chemistry, Condensed Matter Physics, a related field, or equivalent practical experience.

Technical experience in first-principles simulation methods (e.g., DFT and DFPT - Density Functional Perturbation Theory).

Programming experience (e.g., Python) for workflow management, data analysis, and tool automation.

Industry or experience using computational packages like VASP, Quantum ESPRESSO, or similar.

Preferred qualifications:

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.

Familiarity with molecular dynamics (MD) packages like LAMMPS.

A track record of bridging the gap between computational prediction and experimental discovery.