Open Rank - Neuroengineering Cluster- Assistant/Associate/Full Professor

Statistics Interest Group; Chapel Hill (NC); On-site; USD 120,000 - 190,000; Full time

Job summary

UNC Chapel Hill’s School of Data and Information Sciences invites applications for an open-rank tenure-track faculty position in the neuroengineering cluster. The successful candidate will lead AI/ML initiatives translating brain data into clinical insights and work across disciplines to advance neurotechnology.

The role emphasizes developing a funded research program, teaching at multiple levels, and collaboration with clinicians and scientists to impact health outcomes.

Qualifications

• Ph.D. and/or MD required with expertise in AI/ML and data science.
• Strong record in research, teaching, and supervision at undergraduate and graduate levels.
• Ability to secure external funding and work in interdisciplinary teams.

Responsibilities

• Develop and lead an externally funded research program in neuroengineering and AI for brain data.
• Collaborate with clinicians and researchers across departments to advance neurotechnology.
• Translate research into translational outcomes that benefit human health.

Skills

Artificial intelligence

Machine learning

Data science

Computer science

Statistics

Computational neuroscience

Biomedical engineering

Education

Ph.D. or MD

Job description

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UNC Chapel Hill – School of Data and Information Sciences – Open Rank – Neuroengineering Cluster- Assistant/Associate/Full Professor

Company Name UNC Chapel Hill - School of Data and Information Sciences

Company Information

In July 2026, UNC Chapel Hill launched the Division of Data Science and Society within the School of Data and Information Sciences (SDIS), a division devoted to data science teaching, research, scholarship, service, and creativity. DSS’s 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 DSS is to empower a diverse community of faculty conducting research in the fundamentals and/or the applications of data science. The Division 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 Division 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 SDIS include porous borders: leveraging our low walled collaborative Carolina culture to solve major societal problems. Interdisciplinary research clusters that cross Division and School boundaries are a vital element of our Scholarly community. The School of Data and Information Sciences 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.

Duties and Responsibilities

The Division of Data Science and Society (DSS) at UNC-Chapel Hill’s School of Data and Information Sciences (SDIS) is excited to announce an open-rank tenure-track faculty position as part of the University’s neuroengineering cluster hire initiative. This cluster unites the Lampe Joint Department of Biomedical Engineering, the Department of Computer Science, the Department of Neurosurgery, and DSS to build a collaborative, interdisciplinary hub in neuroengineering, focused on brain-computer interfaces (BCIs), brain function imaging and interpretation, closed-loop neuromodulation, and the translation of neuroengineering innovations into improved human health. Within this cluster, the DSS position will provide leadership in machine learning, statistical, and AI frameworks that transform complex large-scale, multi-modal neuroscience and behavioral data into clinically relevant insights, addressing core challenges of heterogeneity, dimensionality, harmonization, and data governance. This position complements cluster strengths in neuroengineering devices, real-time computing systems, and clinical translation, serving as a link between data acquisition, scientific discovery, and application.

The successful candidate will work with cluster faculty to develop and sustain an externally funded research program focused on understanding, predicting, and translating brain function into large-scale neural and behavioral data. Research areas of interest include machine learning and statistical inference for neural data, multimodal neural data integration and harmonization, computational modeling of cognition and behavior, neuroimaging analytics, adaptive neuromodulation, foundation models for neural and behavioral data, and related approaches that advance understanding of brain function and support neurotechnology development. A translational emphasis (such as clinical decision support or neurotechnology deployment) that can position this work to deliver direct, measurable benefits to human health is also desirable. Faculty in this cluster will be recruited with a strong commitment to team science, including joint appointments where appropriate, and coordinated research programs spanning devices, systems, computing, modeling, and translation. Through collaboration among faculty, students, external partners, and the clinical community, the Division of Data Science and Society in SDIS aims to translate research discoveries into tangible societal benefits. We invite you to join the DSS faculty and be part of this mission.

Position Qualifications

The applicants must have a Ph.D. and/or MD with expertise in artificial intelligence/machine learning, data science, computer science, statistics, computational neuroscience, biomedical engineering, or a related field that applies AI and data science techniques.

Preferred applicants will have a strong research record, evidence of interest in developing, teaching, and supervising courses at the undergraduate and graduate levels, and an aptitude in working collaboratively in interdisciplinary research. Research with a focus on machine learning and statistical inference for large-scale, multi-modal neural data is preferred. Expertise in areas such as computational modeling of cognition and behavior, brain-computer interfaces, closed-loop neuromodulation, neuroimaging, and explainable and interpretable AI is preferred. Additionally, candidates must be able to engage with researchers, clinicians, professionals, and stakeholders from diverse disciplines. A record of or the potential to secure external funding to support research projects is strongly preferred.

Salary Range Salary will be commensurate with qualifications and experience.

Application Deadline 11/16/2026