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Research Scientist

ODU Research Foundation
Old Dominion University Research Foundation
Closing date
Feb 26, 2023

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Are you looking to join a growing and exciting Data Science Team? If you are looking for a career opportunity with a unique organization, where your contributions matter, consider Old Dominion University Research Foundation, in Norfolk, VA, which is engaged in a unique partnership with Thomas Jefferson National Accelerator Facility (JLab, a Department of Energy national lab). As a Research Scientist of the ODU/JLab Data Science team, you will participate in data science research and development efforts focused on projects related to population health, clinical health, and coastal resilience.

We are seeking multiple Research Scientists with a PhD degree or greater in Computer Science, Data Science, Applied Mathematics, Computer Engineering, or a closely related technical field. The Research Scientist should have a minimum of 0 to 3 years of experience in applying statistical theory and methods to collect, interpret, and summarize data on projects related to population health, clinical health, and coastal resilience. The Research Scientist will make predictions and recommend actions based on data.

Specific knowledge, skills, and abilities required:

  • Proficiency in Python and familiarity with publicly available technical libraries for data analytic (e.g., scikit-lean), deep learning.
  • Demonstrated ability to work with large datasets and mine relevant information for use in AI/ML applications.
  • Demonstrated ability to develop approaches and solutions to complex problems in the forms of proposals, software, documents, or other work products.
  • Proactive, highly motivated self-starter with demonstrated experience with contributing and leading tasks on major projects with multi-disciplinary teams.
  • Must be able to operate computer equipment in an office or laboratory environment.
  • Ability to clearly communicate and report the progress on tasks and projects.
  • Strong interpersonal skills and ability to effectively work on project teams.
  • Peer-reviewed publication record in Computer Science, Data Science, Applied Mathematics, Computer Engineering, or a closely related technical field.


  • Work on all elements of data science workflow (data preprocessing, analyzing data using exploratory mathematics and statistical techniques
  • Generative and data reduction machine learning methods
  • Anomaly/fault detection
  • Causality methods
  • Apply and develop methods to include uncertainty quantification for scientific research project
  • Determines solutions to problems and provides clear reports on project progress and completion
  • Recommends various technology options or approaches for system and processes improvements in terms of performance, efficiency, cost or safety
  • Publish research results in highly visible, peer-reviewed venues (conferences & journals)
  • Develop and maintain high quality software for data science projects (machine learning & uncertainty quantify)
  • Interact with internal and external researchers and domain scientists for collaboration purposes
  • Participate and potentially lead technical presentations on the work Other duties as assigned

To apply, visit  then click on the employment tab and follow the link. Specify position #22-042. Review of applications will begin immediately and will continue until the position is filled. AA/EOE/M/F/D/V/DFW.

Old Dominion University Research Foundation is an equal opportunity employer (AA/EOE/M/F/Disability/VETS/Drug Free). We pledge our full support to equal employment opportunity for all persons as we recruit, employ, train, compensate, and promote without regard to race, color, religion, sex, national origin, physical and/or mental disability, genetic information, age, protected veteran status or any other basis protected by applicable federal, state or local law


Old Dominion University Research Foundation collaborates with the university for the successful administration of sponsored programs by providing responsive and cost-effective support.

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