Post Doctoral.Post Doctoral.Associate

We are recruiting a Postdoctoral Research Associate to lead and contribute to collaborative, multidisciplinary research on the data-driven, system-level modeling and assessment of energy systems, in support of The Archie Initiative (https://archieinitiative.org/). The successful candidate will work at the interface of process engineering, data science, and sustainability analysis — helping turn large, heterogeneous datasets on oil fields, refineries, power plants, and renewable-energy assets into rigorous, reproducible models of energy-system performance, cost, and greenhouse-gas footprint.

This is a strong opportunity for a recent PhD graduate who wants to apply quantitative, engineering-based methods to real-world energy-transition questions, working alongside an interdisciplinary team of engineers, data scientists, and policy researchers — with direct visibility into an initiative already used by industry, investors, and policymakers.

What You'll Do

• Develop and refine data-driven, system-level models estimating the energy intensity, carbon intensity, and techno-economic performance of segments of the global energy supply chain (extraction, transport, processing/refining, power generation, and end use).
• Build and validate process-simulation models in AspenPlus (or comparable software) representing energy conversion and processing pathways, and translate simulation outputs into life-cycle and techno-economic metrics.
• Conduct life-cycle assessments (LCA) following recognized methodologies (e.g., ISO 14040/14044) to quantify greenhouse-gas and other environmental burdens across energy pathways.
• Apply data science methods — statistical analysis, machine learning, large-scale data curation and integration — to harmonize the public and proprietary datasets feeding the Archie models.
• Formulate and solve optimization problems (e.g., linear/mixed-integer programming) to support scenario analysis, technology comparison, and decision-support tools for stakeholders.
• Collaborate closely with faculty, graduate students, data scientists, and industry partners across institutions participating in The Archie Initiative; participate in regular project meetings and workshops.
• Document methods and results rigorously for reproducibility; co-author peer-reviewed publications, technical reports, and conference presentations.
• Contribute to proposal preparation, progress reports, and stakeholder-facing materials as needed.
• Mentor graduate and undergraduate students on related research as opportunities arise.

• A PhD (completed or near completion) in Chemical Engineering, Mechanical Engineering, Energy Systems Engineering, Environmental Engineering, Systems Engineering, Operations Research, or a closely related field.
• Demonstrated ability to conduct independent, rigorous quantitative research, evidenced by peer-reviewed publications or a strong dissertation record.
• Strong programming/scripting skills (e.g., Python, MATLAB, or R) for data analysis and modeling.
• Excellent written and verbal communication skills in English, including the ability to present technical work to interdisciplinary and non-specialist audiences.
• Demonstrated ability to work both independently and collaboratively within a multidisciplinary, multi-institutional research team.

We especially encourage candidates with demonstrated capability or experience in one or more of the following:

• Data science — statistical modeling, machine learning, or large-scale data wrangling and integration from heterogeneous public/proprietary sources.
• Process simulation via AspenPlus — building, validating, and interpreting process-flow models of energy or chemical conversion systems.
• Life-cycle assessment (LCA) — cradle-to-gate or well-to-wheel GHG and environmental-impact quantification, ideally with tools such as GREET, SimaPro, openLCA, or comparable frameworks.
• Energy systems modeling — representing multi-stage or multi-region energy supply chains, from resource extraction through end use.
• Techno-economic modeling — capital/operating cost estimation, levelized cost of energy or product, and economic feasibility analysis of energy technologies.
• Optimization — formulating and solving linear, nonlinear, or mixed-integer programs (e.g., via Pyomo, GAMS, or Julia/JuMP) for design or planning problems under uncertainty.

Assignment Category

Full-time regular

Campus

Pittsburgh

Child Protection Clearances

Not Applicable

Required Attachments

Cover Letter, Curriculum Vitae