Post Doctoral.Post Doctoral.Associate

Position Summary

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 ( 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.

Required Qualifications

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.

Especially Encouraged Experience

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.