Facilitate the research and work of the quantum computing, machine learning and experimental team to develop algorithms for materials discovery. Work with the computational materials engineering team to introduce surface chemistry and quantum chemistry accuracy into the DFT and Post-DFT Stack.

JOB REQUIREMENTS:

Ph.D. in computational chemistry, physical chemistry, or related discipline. Strong knowledge in quantum chemistry, heterogeneous catalysis, and chemical kinetics.

Experience in one or more computational chemistry software packages including NwChem, CP2K, Orca, GAMASS (US), Octopus, Qchem, Gaussian, Quantum Espresso, VASP, or similar packages. Experience with compiling, testing, and run-time debugging of computational chemistry software packages like NwChem or CP2K on high-performance computing (HPC) resources and subsequent execution of large-scale HPC simulations.

Experience with SLURM or similar resource manager tools, including hyper-threading and efficient parallel job submission on limited resources using SLURM scripts. Proficiency in python and/or Linux/UNIX shell scripting. Self-driven with ability to work independently locally, while also collaborating with a geographically-distributed, multi-disciplinary team. Ability to design and execute the quantum chemistry simulations of the chemical processes at the solid surfaces and interfaces in a limited-resources environment.

DESIRED QUALIFICATIONS

• 5+ years professional experience

• Strong knowledge in photocatalysis, coordination chemistry, and chemical kinetics.

• Experience with ReaxFF and MD simulators like the LAMMPS package.

• Knowledge in using optimized libraries such as INTEL MKL, OpenMPI, Scalapack, OpenBlas etc. for compilation of quantum chemistry software packages.

• Experience with the atomic simulation environment (ASE) and python modules like Scipy, Numpy, and Matplotlib.

• Experience in experimental collaboration and corroboration of simulation results with experimental measurements.

• Experience with QM or HPC simulations in a cloud environment.

• Experience with machine learning methodologies and material property prediction

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