Qualifications

• Doctorate in Materials Science, Physics or related field.
• 4 years of relevant experience in atomistic transport research.
• Proficiency with DFT and specific software: VASP, Quantum Espresso, GPAW, DFTB+, SIESTA/TranSiesta, QuantumATK.
• Hands-on experience with NEGF calculations and electronic transport phenomena.
• Familiarity with integrating machine learning techniques into simulations.

Responsibilities

• Develop a workflow for high-throughput NEGF calculations of semiconductor interfaces.
• Apply simulations to analyze ideal and defective systems with a focus on defects affecting device performance.
• Run simulations with atomistic software and analyze data for material property investigations.
• Collaborate on industry-relevant problems with external stakeholders.
• Coordinate with experimentalists to validate findings through electrical measurements.
• Collaborate within a modeling team to connect atomistic-scale data to larger device contexts.
• Utilize NEGF and machine learning methods to explore electronic transport at a broader scale.
• Package and share software and workflows for the semiconductor research community.

Benefits

• Flexible work hours to accommodate research timelines.
• Opportunity to collaborate with leading industry professionals.
• Access to cutting-edge research tools and software.
• Contributions to impactful semiconductor research and developments.
• Potential for exposure to advanced machine learning applications in research.