The Nuclear Technologies and National Security Directorate (NTNS) is seeking a dynamic and passionate Postdoctoral Appointee with strong background in statistics or machine learning to lead an innovative project aimed at enhancing global and domestic supply chain resilience through the development of advanced supply chain analytical tools. The role will require ability to understand and integrate diverse data sets, including trade, political, economic, and social metrics to identify global risks that impact sourcing strategies essential for achieving economic and climate objectives.
Key Responsibilities:
Spearhead the research and development of predictive models and strategic tools designed to support decision making in strengthening both domestic and international supply chains, particularly focusing on energy technologies.
Collaborate on the creation of a comprehensive supply chain database specifically for targeted energy technologies.
Apply advanced analytics to assess and classify domestic and global sourcing strategies, taking into consideration of various risks and uncertainties.
Partner with an interdisciplinary team to support creation of supply chain databases and develop user-friendly software interfaces that enhance data accessibility of data and insight for diverse stakeholders.
Facilitate ongoing communications and foster relationships with a broad array of stakeholders, including community groups, governmental bodies, and private sector entities.
Conduct rigorous analysis and develop models for improving the domestic and global resilience of supply chains for energy technologies
Support stakeholder engagement and community building activities such as townhalls, info sessions and workshops
Communicate research outcomes through scientific and technical reports, peer-reviewed publications, conference papers and presentations.
What We Offer:
An opportunity to lead groundbreaking research that makes a tangible impact on global and domestic supply chains.
A supportive, collegial environment that encourages innovation and fosters professional growth.
Active participation in a dynamic network of scholars and industry experts committed to addressing critical global challenges through collaboration and innovation.
Position Requirements
This level of knowledge is typically achieved through a formal education in Statistics, Machine Learning, Computer Science, Logistics/Supply Chain, or a related field at the Ph.D. level with zero to five years of employment experience.
Demonstrated experience in leading research initiatives, with a strong track record of publishing in peer-reviewed journals.
Excellent communication skills, capable of crafting and presenting complex information effectively to a variety of audiences.
Ability to work collaboratively in a multidisciplinary team setting.
Commitment to Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Desired Knowledge, Skills, and Experience
Proven capability in convergent thinking and systems analysis, drawing on diverse academic backgrounds such as engineering, computer science, statistics, and economics.
Familiarity with energy technologies and associated supply chain risk and challenges.
Familiarity with the application of statistics and machine learning techniques in analyzing supply chain risk.
Ability to develop and synthesize visualizations to effectively communicate analysis results.
Strong programming skills in statistical languages such as R or Python.
Job Family
Postdoctoral FamilyJob Profile
Postdoctoral AppointeeWorker Type
Long-Term (Fixed Term)Time Type
Full timeAs an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
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