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Military and civilian aviation require the use of head-mounted devices for communication with internal and external sources of information. In these situations, there is an inherent noise that exists due to the aircraft, which influences the method, manner, and quality of the communication signals sent and received. The 711 Human Performance Wing (711 HPW) of the U.S. Air Force Research Laboratory (AFRL) possesses more than two decades of data from two ANSI Standard compliant facilities that define a variety of hearing protection device transfer functions, but through the years, the facilities have been upgraded, which has altered the format of the data. To explore the relationships between speech and the noise that is experienced, this data must be reformatted and combined together. The AFRL is offering a data science and analytics research opportunity to contribute to this project.
What will I be doing?
As an ORISE participant, you will join a community of scientists and researchers in an effort to understand the relationship between hearing protection transfer functions and the variety of meta-data to determine what is the most important element of the device’s manufacturing. Additional analyses will explore a variety of questions that can only be answered after the data has been cleaned and organized into a single common format.
Why should I apply?
Under the guidance of a mentor, you will gain hands-on experience to complement your education and support your academic and professional goals. Along the way, you will engage in activities and research in several areas. These include, but are not limited to:
- Learning how to organize and database existing data for data mining and analytics research.
- Making inferences from available data that will assist in development of machine learning models that will be validated with additional behavioral studies.
- Exploring a variety of research questions that will culminate in research papers and presentations that define new ways to understand and explore acoustic attenuation through human mounted devices.
- Reporting research updates in weekly group meetings, participating in team-based research efforts, and sharing research findings through publications and presentations.
- Taking an active role in designing and conducting experiments, performing data analysis, and adhering to ethical and health safety protocols in the laboratory.
Where will I be located?
Wright-Patterson AFB in Dayton, Ohio
What is the anticipated start date?
AFRL is ready to make appointments immediately. Exact start dates will be determined at the time of selection and in coordination with the selected candidate. Applications are reviewed on an ongoing basis and internships or fellowships will be filled as qualified candidates are identified.
What is the appointment length?
This appointment is a 12-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.
What are the benefits?
You will receive a stipend to be determined by AFRL. Stipends are typically based on a 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
The qualified candidate will have a bachelor’s, master’s, or doctoral degree in computer science, data science, or a related discipline, or will have completed their degree by May 31, 2023. Degree must have been received within three years of the appointment start date.
Highly competitive applicants will have education and/or experience in one or more of the following:
- A minimum of 3 years toward a bachelor’s degree in computer science or data science.
- Experience with machine learning, data analytics, databasing, and high level programming (Python, R).
- Experience in data processing, organization and transformation.
- Strong written and verbal communication of technical ideas.
About AFRL 711 HPW