Requirements:

• 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.

Nice to haves:

• 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.

What you'll be doing:

• 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.

Perks and benefits:

• Google DeepMind is a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development.
• Opportunities for diverse learning and varied career pathways.
• Focused on public benefit, scientific discovery, safety, and ethics.