Catalyst Simulation Postdoctoral Researcher

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SandboxAQ

💵 $115k-$135k
📍Remote - United States

Summary

Join SandboxAQ, a high-growth company delivering AI solutions, as a Catalyst Simulation Postdoc. Lead the implementation of computational workflows to model catalytic materials' reactivity and dynamics. Collaborate with a team of experts using state-of-the-art atomistic modeling techniques. Support client engagements and develop hybrid optimization pipelines. Prepare reports and publications to communicate research findings. This 1-3 year, remote position offers a competitive salary, bonuses, equity, and benefits.

Requirements

  • Currently enrolled or recently completed a STEM PhD Program in Chemical Engineering, Materials Science, Physics, Chemistry, Computational Science, or related fields
  • Intermediate to advanced skill in programming languages (e.g. Python)
  • Proficiency in density functional theory (DFT), molecular dynamics (MD), kinetic modeling, and other computational techniques, with the ability to integrate these with machine learning approaches
  • Significant domain experience modeling chemical reaction networks for at least one of the following catalyst material classes, as indicated by at least one relevant high-impact publication: porous materials, homogeneous transition metal complexes, heterogeneous transition metal or metal oxide surfaces

Responsibilities

  • Lead design and implementation of new computational workflows simulating reactivity and dynamics of catalytic materials at atomic, mesoscale, and continuum scales
  • Support client engagements on the research and development of novel catalysts
  • Collaborate with cross-functional teams to create hybrid optimization pipelines that combine physics-based catalyst simulation approaches, modern machine learning methods, experimental reactor measurements, and experimental catalyst characterization techniques
  • Prepare reports, presentations, and publications to communicate research findings to internal, academic, and industry partners

Preferred Qualifications

  • Experience developing machine learning force fields for solid-state systems is a plus
  • Experience providing in silico support to experimental groups working on catalyst design and/or optimization is a plus

Benefits

  • The US base salary range for this full-time position is expected to be $115k - $135k per year
  • This role may be eligible for annual discretionary bonuses and equity
  • Medical/dental/vision
  • Family planning/fertility
  • PTO (summer and winter breaks)
  • Financial wellness resources
  • 401(k) plans
  • Competitive salaries
  • Stock options depending on employment type
  • Generous learning opportunities
This job is filled or no longer available