Inside Higher Ed
Post-Doc Research Associate
Inside Higher Ed, Chapel Hill, North Carolina, United States, 27517
Post-Doc Research Associate
University of North Carolina at Chapel Hill, Department of Radiology – Research
Location:
Chapel Hill, NC
Position Type:
Postdoctoral Scholar (Full‑time Temporary)
Posted:
September 30, 2025
Position Summary A postdoctoral associate position is available in the group of Dr. Tengfei Li at the University of North Carolina at Chapel Hill. The successful candidate will develop and apply advanced statistical and machine learning methods for infant cognitive prediction using large‑scale multimodal neuroimaging data. The research will emphasize methodological innovation in statistical modeling, transfer learning, machine learning, model ensemble strategies, and interpretable artificial intelligence to improve prediction accuracy while ensuring biological interpretability. The candidate will contribute to building computational frameworks that integrate diverse imaging features, quantify uncertainty, and yield robust and explainable predictions of early‑life cognitive outcomes. The postdoc will primarily work with Dr. Tengfei Li and collaborate closely with Dr. Weili Lin and Dr. Hongtu Zhu.
Minimum Education and Experience Requirements PhD degree in Biostatistics/Statistics.
Required Qualifications, Competencies, and Experience
Familiarity with transfer learning, machine learning, data harmonization, longitudinal data analysis, model ensemble, and interpretable AI
Experience with programming languages: R, Python, or Matlab
Preferred Qualifications, Competencies, and Experience
Strong organizational skills
Special Instructions For information on UNC Postdoctoral Benefits and Services click here.
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Location:
Chapel Hill, NC
Position Type:
Postdoctoral Scholar (Full‑time Temporary)
Posted:
September 30, 2025
Position Summary A postdoctoral associate position is available in the group of Dr. Tengfei Li at the University of North Carolina at Chapel Hill. The successful candidate will develop and apply advanced statistical and machine learning methods for infant cognitive prediction using large‑scale multimodal neuroimaging data. The research will emphasize methodological innovation in statistical modeling, transfer learning, machine learning, model ensemble strategies, and interpretable artificial intelligence to improve prediction accuracy while ensuring biological interpretability. The candidate will contribute to building computational frameworks that integrate diverse imaging features, quantify uncertainty, and yield robust and explainable predictions of early‑life cognitive outcomes. The postdoc will primarily work with Dr. Tengfei Li and collaborate closely with Dr. Weili Lin and Dr. Hongtu Zhu.
Minimum Education and Experience Requirements PhD degree in Biostatistics/Statistics.
Required Qualifications, Competencies, and Experience
Familiarity with transfer learning, machine learning, data harmonization, longitudinal data analysis, model ensemble, and interpretable AI
Experience with programming languages: R, Python, or Matlab
Preferred Qualifications, Competencies, and Experience
Strong organizational skills
Special Instructions For information on UNC Postdoctoral Benefits and Services click here.
#J-18808-Ljbffr