Components of the online application are as follows:
Profile Information
Educational and Employment History
Essay Questions (goals, experiences, and skills relevant to the opportunity)
Resume (PDF)
1 Recommendation(s)
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 ARMY-MRMC@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.
Letter of Recommendation: While a letter of recommendation is not required to be considered, applicants are required to provide contact information for one recommendation in order to submit the application. Applicants are encouraged to request a letter of recommendation before submission as this may help reviewers have a better understanding of the applicant’s qualifications and interests. If selected, a letter recommendation must be submitted on your behalf upon acceptance of the appointment.
The U.S. Army Institute of Surgical Research (USAISR) is one of six research laboratories within the U.S. Army Medical Research and Development Command of the U.S. Army Futures Command. The Institute is the Army's lead research laboratory for improving the care of combat casualties. The mission of the Institute is to "Optimize Combat Casualty." For more information about USAISR: https://www.usaisr.amedd.army.mil.
This is a training opportunity for an established statistician/data scientist with experience in banking and finance or industry (e.g., automotive industry) to learn principles of study design, data analysis, and clinical inference specific to conducting combat casualty care research. This opportunity will train the participant to apply statistical methods to biological processes in a health research environment. The participant will collaborate with scientists and clinicians to learn how to 1) develop relevant and testable trauma-related research questions and hypotheses, 2) design both animal and human research studies, 3) analyze data using a variety of statistical software programs (including but not limited to JMP, SigmaPlot, and GraphPad), 4) apply advanced statistical theories, techniques, and methods to human and animal research data, and 5) interpret clinical research findings. In addition, the participant will assist with preparing a variety of products including study protocols, abstracts, and peer-reviewed publications.
Appointment Length
This appointment is a twelve 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 USAISR. 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.
The participant should be an established scientist with a graduate degree in statistics, applied mathematics, or data science and 5+ years of work experience in banking and finance or industry (e.g., automotive industry). Skills and experience that are favorable include teaching advanced statistics or data science classes, calculating sample size/power, and preparing publication quality tables and figures. A thorough knowledge of the theory and techniques of applied statistical methods such as linear mixed models, generalized estimating equations, analysis of non-parametric data, and failure analysis are required. Strong data management skills as well as knowledge of SAS programming language are highly desirable.
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