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. |
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