A postdoctoral position is now open for candidates with expertise and experience in process modeling, technoeconomic analysis (TEA), and environmental life cycle analysis (LCA).

The Systems Assessment Center (SAC) of Argonne National Laboratory has been conducting assessments of transportation fuels and advanced vehicle technologies over the last 25 years. SAC has developed the GREET® (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) model for estimating energy use, air emissions, and water consumption on a life-cycle basis for conventional vehicle-fuel systems, as well as for emerging technologies for energy production, energy storage, and chemical production (see https://greet.es.anl.gov for details). The GREET model has more than 50,000 registered users worldwide, including academia, industry, and regulatory agencies. SAC has been working closely with industries, universities, and governmental agencies to examine next-generation fuels and chemicals derived from both conventional and alternative energy sources, for enhanced performance and reduced emissions.

SAC remains dedicated to evaluating production technologies for both conventional and advanced fuels, chemicals, and materials, quantifying their energy consumption, environmental impacts, and production cost. Current research efforts include, but are not limited to, the analysis of:

• Production technology of fuels and chemicals using waste CO2 sources and low-carbon electricity.

• Energy consumption and environmental impact of hydrogen production and storage from various technologies and energy sources.

• Regional analysis of waste CO2 sources, cost estimation for CO2 capture (from industrial sources and direct air capture), compression, transportation, and sequestration.

• A variety of production technologies for next-generation fuels and chemicals from waste gas, biomass, plastic and municipal wastes.

• Decarbonization of various industrial and manufacturing sectors (e.g. iron and steel, refinery, etc.).

The candidate will apply chemical engineering principles to design and simulate fuel and chemical manufacturing processes, evaluating process economics and environmental impact.

The candidate will also conduct analysis on energy and production technologies and identify energy and emissions data from several sources. Sources may include federal agencies’ databases, process simulation, technical literature, and collaborations with other national laboratories, academia, industry, and stakeholders. The successful candidate will need to have the ability to problem solve while being able to work both independently and in a group setting. The successful candidate will collaborate with researchers within SAC to expand and enhance the GREET model with novel data.

Position Requirements

• PhD in chemical engineering, petroleum engineering, or related fields.

• Skilled oral and written communication and interpersonal skills at all levels of the organization.

• Demonstrated experience in process modeling and process simulation.

• Knowledge of chemical engineering and transport phenomena principles applied to process equipment design.

• Knowledge of industrial processes and emerging technologies for fuels production, hydrogen production, petrochemical processes, electrochemical processes, or carbon dioxide capture and storage.

• Knowledge of TEA and LCA principles.

• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

• Ability to make our laboratory a safe, welcoming, inclusive, and accessible environment where all can thrive.

Preferred Qualifications:

• Experience using Aspen Plus or Aspen HYSYS.

• Knowledge of oil refinery processes and refinery operations.

• Experience in computational fluid dynamics applied to chemical systems (reactive multiphase systems with and without energy transfer, combustion, etc.)

• Ability to work independently and in a group setting.

• Experience using the GREET model.

• Experience with at least one programming language – Python, VBA, or Fortran.

Job Family

Job Profile

Worker Type

Time Type