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Machine Learning Summer Intern

The mission of the Air Force Institute of Technology (AFIT) is to help build America's airpower, by educating military and civilian Airmen to innovatively accomplish the Air Force's core missions, in support of joint operations, more effectively, efficiently, sustainably and affordably. We provide unique defense-focused, research-enabled, multi-disciplinary advanced academic education, as well as globally delivering career-long, action-based, functional professional continuing education, over a continuum of learning, on-command and on-demand. Our success is measured by the career-long contributions of our graduates, faculty and staff. AFIT accomplishes this mission through four schools: the Graduate School of Engineering and Management, the School of Systems and Logistics, the Civil Engineer School, and the School of Strategic Force Studies. To learn more about the research performed at AFIT, please visit www.afit.edu.

This internship opportunity is offered through the NEAT center, which is positioned at the Air Force Institute of Technology (AFIT).  Under the guidance of a mentor, participants will gain a broad experience in relevant nuclear technologies and engineering at the graduate level as it applies to the US Air Forces and Department of Defense and will benefit from the opportunities to conduct research with AFIT's experienced, professional faculty. The selected candidate may be involved in the support of sponsored research through hands-on experiments, the application of computational analysis techniques, and interaction with government (Military and National Laboratory) and civilian university collaborators. Areas of research are broad, and may include the fields of proliferation of nuclear weapons, nuclear detection, nuclear weapon effects, the nuclear fuel cycle, and/or nuclear power.
 
The research focus for this opportunity is on developing radiation imaging algorithms for low information systems. This imaging is accomplished for localized sources by applying various analysis techniques including the locally competitive algorithm and machine learning (neural networks). The participant will gain an overall familiarity with inverse problems, identification of radiation features and location, and advanced machine learning concepts and analysis such as minimally supervised learning.

Appointment Length
This appointment is a three-month research appointment, with the possibility to be renewed for additional research periods. 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 AFIT. 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.
 
Qualifications
Participants should be current undergraduate or graduate students pursuing degrees or having received their degree in the past two years in a science, engineering, mathematics, or other highly quantitative field. Applicants must have demonstrated exceptional academic performance as well as strong analytical and communication (oral and written) skills. The ability to think creatively and perform research at the graduate level is required. In addition, the applicant should be interested in being part of a fast-paced environment, which is focused on nuclear engineering research, mentoring, and student development.

Eligibility Requirements
  • Citizenship: U.S. Citizen Only
  • Degree: Bachelor's Degree or Master's Degree received within the last 24 months or currently pursuing.