Computational Materials Research Engineer

About the role

We are looking for a motivated individual to join Western Digital Research, a world-class research laboratory in San Jose, California with ~80 employees. Research activities in the lab focus on information storage, sensors, spintronics, superconducting devices, and AI memory/compute architectures.

Responsibilities

• Conducting and developing new capabilities in computational simulations of materials, interfaces and devices, including all forms of transport phenomena (e.g., thermal, electronic, spin).
• Work on AI assisted materials exploration and discovery with a focus on evaluating key material properties and interactions for device design.
• For this work, AI and machine learning techniques will be used to accelerate atomistic material simulations and to conduct rapid assessments of large material candidate pools.
• Work closely with experimental teams to guide material exploration and assist in interpreting experimental results.

Qualifications

• 3+ years or higher experience in computational materials science with strong background in solid-state physics and devices.
• PhD in Physics, Materials Science, Electrical Engineering, Chemistry, or Chemical Engineering.
• Expert knowledge of atomistic simulation techniques such as density functional theory and classical molecular dynamics.
• Experience with ab-initio codes including QuantumATK, VASP, Quantum Espresso, Questaal, and KKR-CPA.
• Familiarity with PAOFLOW or WANNIER90.
• Experience with molecular dynamics codes such as LAMMPS.
• Experience using machine learned interatomic potentials for large scale atomistic simulations is a plus.
• Experience in magnetism and magnetic materials, spin-orbit interactions, spin Hall materials, ordered and disordered materials, electronic, spin and thermal transport.
• Knowledge of chemical interactions at material interfaces and surfaces would also be an asset for a strong candidate.
• Experience predicting transport properties (e.g. spin Hall conductivity, anomalous Hall conductivity) from first principles using either non-equilibrium Green’s function techniques (NEGF) or Kubo-Greenwood formalism would also be a plus.
• C competent programming skills and familiarity with high performance computing environments.
• Experience coding in Python and working with material simulation environments like ASE would be highly beneficial.
• Excellent written communication and organizational skills.
• High attention to detail.
• Strong work ethic.