Job Description
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.
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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.
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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.
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Minimum qualifications:
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PhD in Computational Materials Science, Solid-State Chemistry, Condensed Matter Physics, a related field, or equivalent practical experience.
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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.
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Industry or experience using computational packages like VASP, Quantum ESPRESSO, or similar.
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Preferred qualifications:
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Experience in developing or applying machine learning models for materials property prediction.
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Experience with high-throughput computational workflows and running simulations on HPC or cloud infrastructure.
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Familiarity with molecular dynamics (MD) packages like LAMMPS.A track record of bridging the gap between computational prediction and experimental discovery.
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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.
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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.
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PhD in Computational Materials Science, Solid-State Chemistry, Condensed Matter Physics, a related field, or equivalent practical experience.
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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.
n
Industry or experience using computational packages like VASP, Quantum ESPRESSO, or similar.
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