Predoctoral Appointee

Argonne National Laboratory

Lemont, USA

Job posting number: #7205791 (Ref:417051)

Posted: January 10, 2024

Job Description

Argonne’s Vehicle and Mobility Systems department (VMS, vms.taps.anl.gov) is looking for a Predoctoral Appointee with data modeling experience to participate in the development of a software framework for the evaluation of large-scale data modeling and analytics. The position requires familiarity with data engineering, processing, and analysis tools to build efficient data workflows. It is preferred if the candidate has familiarity with supervised and unsupervised machine learning techniques for data mining, analysis, outlier detection, and imputation.

Job duties include:

    • Collecting latest data from various sources and expanding platform to include new sources.

    • Scaling current workflows for production; automation of test execution, monitoring, data collection, data processing, analysis, quality check.

    • Improving data accessibility by introducing RDBMS architecture into a platform for more streamlined downstream analysis.

    • Leveraging and improving analytical models used in platform to streamline data from various data sources, clean, process, store, and query large-scale datasets. 

    • Development of different analytical models and methods to streamline data from various data sources, clean, process, store, and query large-scale datasets.

    • Development of an integrated program based in Python to evaluate different analytical case scenarios based off large scale vehicle simulations.

    • Integration of simulation tools into the framework.

    • Formalization and automation of workflows for the setup of experiments, test execution, monitoring, data collection, data processing, analysis, quality check.

    • Integration of visualization components for large-scale data.

    The candidate will work with researchers that develop new data across different large-scale studies to demonstrate them, actively participating in their deployment from pure simulation to detailed visualization. The engineer will also demonstrate skills of supervised and unsupervised machine learning techniques for data mining, analysis, outlier detection, and imputation.

    Position Requirements

    • Master’s Degree.

    • Experience in systems engineering in automotive, aerospace, robotics or related applications.

    • Experience with different data modeling, pipelining, and cleaning methods.

    • Experience with different data visualization methods.

    • Programming in C/C++, Python (pymongo, scrapy, pandas, fuzzywuzzy, etc.).

    • Experience with Python-Selenium, MongoDB, PostgreSQL, MySQL, GitHub, Apache Airflow.

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

    Preferred Qualifications:

    • Experience as an engineering research associate.

    • Experience with data visualization, database management, big data analytics, machine learning, exploratory analysis, etc.

    • Experience with Software life cycle (source control, documentation, deployment).

    • 1-2 years using web scraping frameworks such as scrapy.

    • 1-2 years with regular expressions, string matching/comparison.

    • 1-2 years developing SQL and NoSQL architectures.

    • 1-2 years using Apache Airflow (or similar scheduling software) to automate Python workflows.

    • 1-2 years building production workflows (e.g., scalable solutions) using Python.

    • 1-2 years working with supervised or unsupervised statistical modeling.

    • 0-1 years working with natural language processing techniques.

      Job Family

      Temporary Family

      Job Profile

      Predoctoral Appointee

      Worker Type

      Long-Term (Fixed Term)

      Time Type

      Full time

      As 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.

      Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.  

      All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis.  Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements.  Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.



      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.


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