Graduate Student Research Assistant

School of Medicine

 

Established in 1930, Duke University School of Medicine is the youngest of the nation's top medical schools. Ranked sixth among medical schools in the nation, the School takes pride in being an inclusive community of outstanding learners, investigators, clinicians, and staff where interdisciplinary collaboration is embraced and great ideas accelerate translation of fundamental scientific discoveries to improve human health locally and around the globe. Composed of more than 2,600 faculty physicians and researchers, nearly 2,000 students, and more than 6,200 staff, the Duke University School of Medicine along with the Duke University School of Nursing, and Duke University Health System comprise Duke Health, a world-class academic medical center. The Health System encompasses Duke University Hospital, Duke Regional Hospital, Duke Raleigh Hospital, Duke Health Integrated Practice, Duke Primary Care, Duke Home Care and Hospice, Duke Health and Wellness, and multiple affiliations.

 

Be You.

 

Duke Health’s Collaborative to Advance Clinical Health Equity (CACHE) is seeking a dedicated and detail-oriented individual to join our team as a Clinical Research Intern. This position offers a unique opportunity to contribute to research aimed at addressing healthcare-related disparities by improving quality of care.

 

This is a Clinical Research internship opportunity for recent college/university graduates. Using a multidisciplinary model, incumbents are assigned responsibilities designed to provide exposure to and gain an overall understanding of the lifecycle of a clinical research project. The internship will generally involve a combination of learning and research support activities under the mentorship of a Principal Investigator (PI). Incumbents will receive regular and ongoing feedback from the mentor and are expected to produce a publication based on their learning experience.

 

This is a full-time position and the maximum duration of the internship is 2 years.

 

Work Arrangement – Hybrid

 

Anticipated Pay Range - Duke University provides an annual base salary range for this position as USD $18.00 to USD $26.49. Duke University considers factors such as (but not limited to) scope and responsibilities of the position; candidate's work experience, education/training, and key skills; internal peer equity; as w ell as market and organizational considerations when extending an offer.

 

Your total compensation goes beyond the dollars on your paycheck. Duke provides comprehensive and competitive medical and dental care programs, generous retirement benefits, and a wide array of family-friendly and cultural programs to eligible team members.

 

Learn more at: https://hr.duke.edu/benefits/

 

PREFERRED QUALIFICATIONS

 

• Prefer a currently enrolled Master's student at Duke University in the Department of Biostatistics and Bioinformatics. 
• Strong analytical skills and attention to detail, with the ability to critically evaluate research findings.
• Familiarity with data visualization techniques and tools is preferred.
• Excellent communication skills, both verbal and written, with the ability to collaborate effectively within a multidisciplinary team.
• Demonstrated interest in public health, quality and equity in healthcare and process improvement.

 

MINIMUM QUALIFICATIONS

 

Education/Training - Work requires an Associate degree.

 

A Bachelor degree may substitute for this degree and 2 years of experience.

 

Experience – See job description.

 

OR AN EQUIVALENT COMBINATION OF RELEVANT EDUCATION AND/OR EXPERIENCE

 

Be Bold.

 

Duke AI Health connects, strengthens, amplifies, and grows multiple streams of theoretical and applied research on artificial intelligence (AI) and machine learning (ML) to answer the most urgent and difficult challenges in medicine and population health. Designed as a multidisciplinary, campus-spanning initiative, AI Health harnesses expertise and insights across multiple schools, centers, and institutes at Duke to bring to bear the power of machine learning and related quantitative fields on medicine, healthcare delivery, and the health of individuals and communities. Our transdisciplinary approach