Entry-Level – Researcher, Aftertreatment Catalysis
Core
Design and execute laboratory studies on automotive aftertreatment catalysts, applying AI/ML methods to guide catalyst formulation and system integration for emissions reduction.
Role type
Entry-level researcher (catalysis & AI/ML)
Builds
Next-generation aftertreatment catalysts and predictive modeling tools
Domain
Automotive emissions control / Heterogeneous catalysis
Deliverable
production ML models
Required skills
Heterogeneous catalysis, reaction engineering, materials chemistry, catalyst characterization (FTIR-DRIFTS, BET, chemisorption, microscopy), scientific programming (Python/MATLAB/R), AI/ML for science
Preferred skills
Three-way catalysts, flow reactor evaluation, Bayesian optimization, kinetic modeling, high-dimensional data analysis
Technologies
FTIR-DRIFTS, BET surface area analysis, chemisorption, transmission electron microscopy, Python, MATLAB, R
Responsibilities
Design and execute catalyst evaluation studies, develop catalyst structures to reduce platinum content, correlate composition with performance, build datasets for reproducible modeling, apply AI/ML to guide experimental planning, validate AI/ML hypotheses experimentally, collaborate on predictive tools
Seniority
Entry-level, recent PhD graduate
