美国阿贡国家实验室博士后职位—制造业和能源供应链

美国阿贡国家实验室博士后职位—制造业和能源供应链

美国阿贡国家实验室（Argonne National Laboratory，简称ANL）是美国政府最早建立的国家实验室，也是美国最大的科学与工程研究实验室之一——在美国中西部为最大。阿贡前身是芝加哥大学的冶金实验室 (Metallurgical Lab)，现在隶属于美国能源部和芝加哥大学。诺贝尔物理学奖得主费米于1942年在此领导小组建立了人类第一台可控核反应堆（芝加哥一号堆，Chicago Pile-1)，完成了曼哈顿计划的重要一环，并且使人类从此迈入原子能时代。

Postdoctoral Appointee- Manufacturing and Energy Supply Chain

Argonne National Laboratory

Job Number: 7139627 (Ref #: 415611)

Application Deadline: Open Until Filled

Job Description

The Nuclear Technologies and National Security Directorate (NTNS) and the Advanced Energy Technologies (AET) Directorate is seeking a highly motivated postdoctoral appointee. The position will support both the NTNS and AET Directorates with supply chain modeling and analysis, with a focus on critical materials, supply chains and manufacturing associated with energy sector industrial base (ESIB) technologies and U.S. decarbonization goals. In this role you will:

·         Conduct research and analysis and develop models to aid decision-making around topics related to improving the domestic and global resilience of supply chains for technologies vital to clean energy and decarbonization of the U.S. economy.

·         Provide technical and policy expertise to Directorate in areas related to manufacturing and energy supply chains and related technologies.

·         Provide analysis to understand and inform stakeholders of the economic, environmental, and social justice impacts of deploying various strategies to mitigate risks to achieving the nation’s decarbonization goals.

·         Provide research that builds on existing datasets, analysis, and computational models developed by the Argonne, DOE national laboratory collaborators and federal sponsor teams.

·         Develop and apply analytical approaches, compiles results, prepares reports, publications and documentation that evaluate U.S. decarbonization goals and ESIB technology supply chains and quantify impacts of U.S. policies.

·         Collaborate with researchers from other DOE Nationals Laboratories and contribute to interdisciplinary teams.

·         The candidate will receive a supportive and enabling environment to develop research projects, grow research collaborations, communicate impactful research outcomes in peer-reviewed journals, and support other related projects within the team’s portfolio.

Position Requirements

·         Interested candidates must have a solid technical background in a relevant field and a strong desire to apply that background to manufacturing and energy sector supply chains or critical materials research.

·         Knowledge of economic and engineering principles associated with manufacturing and energy sector supply chains.

·         Technology areas of interest include energy storage, wind turbines, fuel cells, electrolyzers, electric vehicles, rare earth magnets, platinum group metal catalysts, semiconductors, solar photovoltaics, transformers and high voltage DC transmission equipment, biofuels, renewable diesel, carbon capture, wave energy, green aluminum and steel, hydrogen refueling stations, heat comps, and others.

·         Cross-cutting topics of interest: life-cycle analysis, techno-economic analysis, process development, industry 4.0, workforce development, design for circulatory, competitiveness, productivity, energy equity and environmental justice (EEEJ).

·         The ideal candidate should have a record of scholarly work in supply chain and/or economic analysis, criticality, computational modeling, scenario analysis, or policy analysis or a strong, transferrable industry experience within supply chain, ESIB technology or equivalent area.

·         Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

·         Skilled oral and written communication skills in scientific and engineering field.

·         Ability to integrate diverse knowledge, methods, and perspectives to drive analysis and innovation.

·         Ability to support research projects, establish collaborations, and work with multidisciplinary research teams.

This level of knowledge is typically achieved through a formal education in economics, mathematics, systems engineering, environmental science/engineering, or any relevant engineering and computational sciences field at the Ph.D. Level with zero years of employment experience. Policy degrees may be considered with relevant quantitative background.

Preferred Qualifications:

·         Candidates with industrial experience related digital supply chain transformation, operations, end-to-end supply chain visibility, resiliency planning is desirable, including recognized industry designations from professional organizations.

·         Specialized knowledge of metals and materials markets and supply chains or the manufacturing of clean energy technologies would be preferred.

·         Experience or familiarity with techno-economic and life cycle analysis is desirable.

·         Experience with manufacturing supply chain analysis or ESIB technology supply chain or manufacturing experience.

·         Manufacturing or supply chain policy expertise.

·         Skill in organizational and time management, with careful attention to detail and the ability to handle multiple stakeholders and priorities and experience working independently under time constraints to meet critical deadlines.

·         Experience with economic analysis techniques (input-output, optimization, cost modeling, econometrics, computational modeling, etc.).

·         Experience with modeling and simulation for analysis of manufacturing supply chains.

·         Specialized experience evaluating specific energy technologies and/or critical materials.

·         Familiarity with technology transfer and commercialization concepts.

·         Familiarity with techno-economic and/or life-cycle analysis.

·         Experience with modern scientific programming languages (e.g., R, Python, Java, Julia).

·         Ability to develop and synthesize visualizations to effectively communicate analysis results.

·         Experience with optimization, operations research, or other computational modeling techniques, such as agent-based modeling, general equilibrium modeling, etc.

This position description documents the general nature and level of work but is not intended to be a comprehensive list of all activities, duties, and responsibilities required of the incumbent. Consequently, the incumbent may be required to perform other duties as assigned.

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