About the Role
We are seeking a Catalyst Simulation Postdoc to lead the implementation of computational workflows to model reactivity and dynamics of catalytic materials. You will work closely with a team of subject matter experts, computational scientists, software developers, and machine learning experts using state-of-the-art atomistic modeling techniques to accelerate the design and optimization of heterogeneous and homogeneous catalysts, focusing on porous materials (zeolites & metal-organic frameworks), transition metal complexes, and transition metal or metal oxide surfaces. This role presents an excellent opportunity for a PhD student in their final year of study or a recent PhD graduate to participate in the high-impact engagements with industrial customers as well as in the establishment of new computational capabilities such as machine learning force fields.
Core 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.
Minimum Qualifications
- 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.
- 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.
Details
- Start date: Year-round, on a rolling basis
- Duration: 1–3 years
- Location: Remote
The US base salary range for this full-time position is expected to be $115k - $135k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.
