Postdoc: AI-Driven Catalysis & Autonomous Lab Research

Northeastern University; Boston (MA); On-site; USD 60,315 - 85,192; Part time

Job summary

Northeastern University in Boston invites applications for a Postdoctoral Research Associate to lead experimental heterogeneous-catalysis research within autonomous, closed-loop laboratory workflows. The role combines catalyst synthesis, high-throughput testing, and in situ characterization with AI methods to accelerate discovery of catalytic materials for energy and sustainability.

The successful candidate will design/operate reactors, apply machine-learning to data, and contribute to

Qualifications

• PhD in Chemical Engineering, Chemistry, Materials Science, or 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 and operate reactors to measure activity, selectivity, and kinetics.
• Develop and run closed-loop autonomous workflows with active-learning and Bayesian-optimization pipelines.
• Build AI workflows and FAIR data infrastructure for instrument orchestration and data curation.
• Analyze data to extract structure-reactivity relationships and interpret mechanisms.
• Disseminate results through publications and mentor researchers.

Skills

Catalysis knowledge

Reactors design

Python programming

Machine learning

Independent work

Education

PhD required

Tools

High-throughput testing

Job description

Northeastern University in Boston invites applications for a Postdoctoral Research Associate to lead experimental heterogeneous-catalysis research within autonomous, closed-loop laboratory workflows. The role combines catalyst synthesis, high-throughput testing, and in situ characterization with AI methods to accelerate discovery of catalytic materials for energy and sustainability.

The successful candidate will design/operate reactors, apply machine-learning to data, and contribute to