Postdoctoral Appointee in Statistical Modeling

Argonne National Laboratory

Argonne, IL

ID: 7103911 (Ref.No. 413572)
Posted: June 22, 2022
Application Deadline: Open Until Filled

Job Description

We seek to hire a postdoctoral appointee at the Mathematics and Computer Science Division at Argonne National Laboratory. In this role you will develop statistical and machine learning techniques along with associated software in order to characterize, detect and model extreme events of complex systems. In particular, focused on environmental extremes and characterize their impacts on human systems and infrastructures such as power grids. You will be challenged by large datasets, complex interactions of extremes and highly non-linear phenomena. 

 

You will work with a team of statisticians, applied mathematicians and computational scientists based at Argonne and in collaboration with Rutgers University, and will have access to world-class high-performance computing resources.

Position Requirements

  • A recent or soon-to-be-completed Ph.D. in Statistics, Applied Mathematics or Data Science and 0+3 years of experience
  • Expertise in statistical extremes and statistical modeling
  • Experience with scientific data analysis (computer model simulations, observational data)
  • Knowledge of machine learning techniques and software (e.g. Python, Julia)

Job Family

Postdoctoral Family

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

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Argonne is an equal opportunity employer, and we value diversity in our workforce. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne prohibits discrimination or harassment based on an individual's age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.