Postdoctoral Research Associate - AI Catalysis

Northeastern University•Boston, MA

4d•$60,315 - $85,193•Onsite

About The Position

Under the direction of the Principal Investigator, the Postdoctoral Research Associate designs and conducts experimental heterogeneous-catalysis research within autonomous, closed-loop (“self-driving”) laboratory workflows. The role integrates catalyst synthesis, high-throughput reactor testing, and in situ/operando characterization with machine-learning and agentic artificial intelligence (AI) methods that propose, prioritize, and interpret experiments, accelerating the discovery and optimization of catalytic materials for energy and sustainability applications. The position contributes to scholarly publications, sponsored-research deliverables, and the training of graduate and undergraduate researchers.

Requirements

PhD in Chemical Engineering, Chemistry, Materials Science, or a closely related field
Knowledge of heterogeneous catalysis, reaction kinetics, thermodynamics, and structure–reactivity relationships
Ability to design and operate catalytic reactors
Ability to synthesize and characterize catalysts
Proficiency in Python programming
Ability to apply machine-learning methods to scientific data
Ability to work independently and across disciplines

Responsibilities

Design, synthesize, and test heterogeneous catalysts (e.g., supported metals, oxides, single-atom systems, and high-entropy metal alloy systems) and operate catalytic reactors to measure activity, selectivity, and kinetics for targeted thermo- and/or electrocatalytic reactions
Develop and run closed-loop autonomous workflows that couple automated synthesis, high-throughput screening, and online/in situ characterization with active-learning and Bayesian-optimization pipelines to guide experiment selection
Build agentic artificial intelligence (AI) workflows and FAIR experimental data infrastructure for instrument orchestration, automated data curation, and autonomous hypothesis generation linking synthesis, characterization, and performance
Analyze experimental and characterization data to extract structure–reactivity relationships and connect empirical findings to mechanistic and kinetic interpretation
Disseminate results through peer-reviewed publications and presentations, contribute to proposals and reports, and help mentor graduate and undergraduate researchers and steward shared instrumentation