Semiconductor Material Science Research Scientist

Snapshot

Science is at the heart of everything we do at Google Deep Mind. From the beginning, we took inspiration from science to build better algorithms, and now, we want to use our toolkit to accelerate scientific discovery. By bringing together specialists with backgrounds in machine learning, computer science, physics, chemistry, biology and more, we’re optimistic that we can build new methods that will push the boundaries of what is possible and help solve the biggest problems facing humanity.

Google Deep Mind (GDM) is pursuing a ground-breaking research program in materials, aiming to accelerate the discovery of new functional materials by combining the predictive power of artificial intelligence (AI) and computational simulation with automated experimentation. The team is establishing experimental capacity to create a closed-loop, AI-driven discovery engine. Computational simulation is critical for grounding the AI and providing quick in silico feedback before materials are sent off to the lab for experimental validation.

We are seeking a highly motivated computational materials scientist, with experience designing semiconductor materials, to join our discovery efforts. This role is focused on hands‑on modeling and in‑depth analysis to drive real‑world discovery of next‑generation materials for advanced semiconductor applications. You will be a key contributor to our team, bringing insight into key semiconductor material problems, running advanced simulations, and closely collaborating with senior computational researchers, experimentalists, and AI specialists to drive our mission towards breakthrough material discoveries.

• Semiconductor Materials Expertise:
  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.
• End‑to‑End Discovery:
  Bridging the gap between theory and reality by using computational tools to identify candidate semiconductor materials and working with experimentalists to synthesize them in the lab.
• Hands‑on Simulation & Analysis:
  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.
• Workflow Execution:
  Utilize and help refine state‑of‑the‑art computational tools and automated, high‑throughput workflows on our large‑scale compute infrastructure.
• Data Generation & Integrity:
  Ensure the generation of high‑quality, reproducible computational data from your simulations. Contribute to structuring and curating simulation databases to train next‑generation AI models.
• Cross‑functional
  
  Collaboration:
  
  Work closely with a diverse team of software engineers, AI specialists, computational researchers, and experimental material scientists.
• Reporting & Communication:
  Clearly and efficiently report on computational progress, new material predictions, and challenges to the wider material discovery team.

In order to set you up for success as a Research Scientist at Google Deep Mind, we look for the following skills and experience:

• A PhD in Computational Materials Science, Solid‑State Chemistry, Condensed Matter Physics, or a related field.
• A deep understanding of material requirements and material processes in the semiconductor industry.
• Strong technical expertise in first‑principles simulation methods (especially DFT and DFPT - Density Functional Perturbation Theory).
• Hands‑on experience using computational packages like VASP, Quantum ESPRESSO, or similar.
• Strong programming skills (e.g., Python) for workflow management, data analysis, and tool automation.
• Demonstrated ability to manage and execute computational research tasks effectively, from simulation setup to data analysis and communication.
• Excellent teamwork and communication skills, with a desire to work in a fast‑paced, interdisciplinary collaborative environment.

• A track record of bridging the gap between computational prediction and…