Statistical and Data Visualization Graduate Student Research

Organization
U.S. Department of Defense (DOD)
Reference Code
AFRL-1357613005
How to Apply

Components of the online application are as follows:

Submitted documents must have all social security numbers, student identification numbers, and/or dates of birth removed (blanked out, blackened out, made illegible, etc.) prior to uploading into the application system.

If you have questions, send an email to Airforce@orise.orau.gov. Please list the reference code of this opportunity in the subject line of the email.

All documents must be in English or include an official English translation.

Description

The Air Force Research Laboratory (AFRL) is the Air Force's only organization wholly dedicated to leading the discovery, development, and integration of warfighting technologies for our air, space and cyberspace forces. They trace their roots to the vision of airpower pioneers who understood science as key to air supremacy. The passionate commitment of AFRL people to realize this vision has helped create the world's best air, space and cyberspace force. For more information about AFRL, please visit https://www.wpafb.af.mil/afrl/.

Under the direction of the mentor, the participant will assist in developing a novel way of visually representing statistical results beyond the classical plots and graphs, or obscure statements of inference, that accompany many statistical tests. The goal is to create reliable and rapid representations of analytical results that are readily visualized and easily understood. This will build upon a current program aimed at formulating a new way of visualizing a distribution of random variables that can simultaneously express the many statistical characteristics associated with it while allowing non-experts to quickly glean the pertinent information for decision making. This new method will include an automated component for the data ingestion that decides the appropriate descriptive statistical measures and tests to be computed.

During this appointment, the participant should expect to gain experience in conducting research at a government research facility and opportunities to collaborate with researchers from other branches of 711HPW as well as the Air Force Institute of Technology. Under the direction of the mentor, the participant will also have the opportunities to present their work at Statistics and Data Visualization conferences and meet possible future collaborators.

Appointment Length

This is a twelve month research appointment. Appointments may be extended depending on funding availability, project assignment, program rules, and availability of the participant.

Participant Benefits

Participants will receive a stipend to be determined by AFRL. Stipends are typically based on the participant’s academic standing, discipline, experience, and research facility location. Other benefits may include the following:

  • Health Insurance Supplement. Participants are eligible to purchase health insurance through ORISE.
  • Relocation Allowance
  • Training and Travel Allowance

Nature of Appointment

The participant will not enter into an employee/employer relationship with ORISE, ORAU, DOD, or any other office or agency.  Instead, the participant will be affiliated with ORISE for the administration of the appointment through the ORISE appointment letter and Terms of Appointment.

While participants will not enter into an employment relationship with DOD or any other agency, this opportunity will require a suitability investigation/background investigation. Any offer made is considered tentative pending favorable outcome of the investigation.

Qualifications

Applicants should have be currently pursuing a graduate degree in statistics, data visualization, computer science, or mathematics. 

Point of Contact
Eligibility Requirements
  • Citizenship: U.S. Citizen Only
  • Degree: Bachelor's Degree, Master's Degree, or Doctoral Degree received within the last 60 months or currently pursuing.
  • Minimum Overall GPA: 2.50
  • Discipline(s):
    • Computer, Information, and Data Sciences (16 )
    • Mathematics and Statistics (10 )
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