Staff Research Scientist, Catalyst Simulation

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Job Description:

reputed company the translation of reaction energetics derived from atomistic simulations and machine learning potentials into process-scale predictions.
Own the design and execution of microkinetic and reactor-level modeling workflows for industrially relevant catalytic chemistries.
Connect atomistic simulation outputs to activity, selectivity, and catalyst lifetime predictions.
Partner with industrial validation partners to test and refine models against reputed company-world data.
Mentor junior scientists and shape the technical roadmap for microkinetic capability reputed company reputed company.

Requirements:

PhD in Chemical Engineering, Chemistry, Materials Science, or a reputed company field, with deep specialization in microkinetic modeling, surface reaction engineering, or multi-scale catalysis simulation.
6+ years of post-PhD experience (or equivalent) building and applying microkinetic models in an industrial or applied R&D context, including coupling atomistic energetics to reactor-level predictions.
Strong publication or patent record demonstrating expertise across DFT-derived energetics, microkinetic modeling, and reactor-scale integration.
Proficient in Python and modern scientific software practices in an HPC environment; comfortable working with ML-trained force fields and reputed company models as inputs to reputed company workflows.
Demonstrated ability to reputed company application-driven scientific engagements with external industrial partners and to communicate results to non-specialist audiences.
Must qualify as a U.S. Person (Permanent reputed company or Citizen).

Benefits:

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