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Research Scientist

University of North Carolina at Chapel Hill

University of North Carolina at Chapel Hill

USD 60k-70k / year
Posted on Mar 2, 2026

Research Scientist

Posting Information

Posting Information
Department School of Data Sci and Society - 397100
Career Area Research Professionals
Application Deadline 03/09/2026
Position Type Temporary Staff (EHRA NF)
Position Title Research Scientist
Position Number 20074268
Vacancy ID N000839
Full-time/Part-time
FTE 1
Hours Per Week 40
Position Location North Carolina, US
Hiring Range $60,000 - $70,000
Proposed Start Date 06/01/2026
Estimated Duration of Appointment 12 months
Position Information
Be a Tar Heel!
A global higher education leader in innovative teaching, research and public service, the University of North Carolina at Chapel Hill consistently ranks as one of the nation’s top public universities. Known for its beautiful campus, world-class medical care, commitment to the arts and top athletic programs, Carolina is an ideal place to teach, work and learn.
One of the best college towns and best places to live in the United States, Chapel Hill has diverse social, cultural, recreation and professional opportunities that span the campus and community.
University employees can choose from a wide range of professional training opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks that include numerous retail and restaurant discounts, savings on local child care centers and special rates for performing arts events.
Primary Purpose of Organizational Unit
In 2022, UNC Chapel Hill launched the School of Data Science and Society (SDSS), a new school devoted to data science teaching, research, scholarship, service, and creativity. The SDSS vision is to be a leader in shaping the field of data science through an interdisciplinary and rigorous grounding in theory and methods with a human centric approach to the entire data life cycle.

The mission of SDSS is to empower a diverse community of faculty conducting research in the fundamentals and/or the applications of data science. The school is training undergraduate, graduate, and professional students to be the next generation of data science leaders with the knowledge and skills to thrive in this data-driven world. The SDSS will serve the state, the nation, and the world through premier data science educational programs and innovative research directed to advancing the public good with human-centric and ethical applications.

The core elements of the SDSS include porous borders – leveraging our low-walled collaborative Carolina culture to solve major societal problems. Interdisciplinary research clusters that cross disciplines and school boundaries are a vital element of the school. The SDSS is focused on students and education – the emphasis is not just on data science majors but on all students becoming data literate. The school’s culture has an open and transparent structure, governance, and business model.
Position Summary
The position focuses on developing and applying advanced machine learning techniques to improve full-waveform inversion (FWI) across a range of imaging domains, including geophysics and medical ultrasound. The successful candidate will be responsible for designing data-driven models that enhance the accuracy, robustness, and computational efficiency of FWI workflows. Key duties include integrating deep learning with physics-based modeling, implementing scalable training strategies, and validating methods on both simulated and experimental datasets. The role also involves close collaboration with domain experts in imaging sciences and high-performance computing to advance next-generation inversion methodologies. Additional responsibilities include publishing research findings in top-tier journals and conferences.
Minimum Education and Experience Requirements
Relevant post-Baccalaureate degree required (or foreign degree equivalent); for candidates demonstrating comparable independent research productivity, will accept a relevant Bachelor’s degree (or foreign degree equivalent) and 3 or more years of relevant experience in substitution. May require terminal degree and licensure.
Required Qualifications, Competencies, and Experience
N/A
Preferred Qualifications, Competencies, and Experience
- Ph.D. in data and information science, applied mathematics, computer science, geophysics, biomedical engineering, or a related field
- Expertise in inverse problems, machine learning, and wave physics simulations
- Experience with numerical methods and solving PDEs
- Familiarity with full-waveform inversion and high-performance computing
- Proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow)
- Strong publication record and ability to conduct independent research
- Effective communication and collaboration skills across disciplines
Special Physical/Mental Requirements
N/A
Campus Security Authority Responsibilities
Not Applicable.
Special Instructions
Quick Link https://unc.peopleadmin.com/postings/313829
Contact Information
Office of Human Resources Contact Information
If you experience any problems accessing the system or have questions about the application process, please contact the Office of Human Resources at (919) 843-2300 or send an email to employment@unc.edu
Please note: The Office of Human Resources will not be able to provide specific updates regarding position or application status.
Equal Opportunity Employer Statement
The University is an equal opportunity employer and welcomes all to apply without regard to age, color, gender, gender expression, gender identity, genetic information, national origin, race, religion, sex, or sexual orientation. We encourage all qualified applicants to apply, including protected veterans and individuals with disabilities.

Optional and Required Documents

Required Documents
  1. Curriculum Vitae / Resume
  2. Cover Letter
  3. List of References
Optional Documents

Supplemental Questions

Required fields are indicated with an asterisk (*).

  1. * Please select the response below that describes your level of education that best or mostly closely satisfies the education requirements for this position.
    • Bachelor’s degree in required discipline(s) listed or related field
    • Bachelor's degree in any field/discipline
    • Master's degree or Doctorate degree in required discipline(s) listed or related field
    • Master's degree or Doctorate degree in any field/discipline
    • None of the above