Semiconductor Material Science Research Scientist, DeepMind

• 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.

• 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.