Visiting Student/Scholar Positions
– Machine Learning, Pattern Recognition, and Biomedical Data Analysis
Visiting student/scholar positions are available in the Department of Radiology and Biomedical Research Imaging Center (BRIC) at the University of North Carolina at Chapel Hill. Our current focuses are to identify early biomarkers of abnormal neurodegenerative and neurodevelopmental diseases by developing novel machine learning and deep learning methods. The new positions are for the further development of medical data adaptation/harmonization, multi-modality data fusion, biomedical image retrieval, disease diagnosis/prognosis, and image processing.
- The successful candidate should have a strong background in Biomedical Engineering or Computer Science, preferably with an emphasis on medical image feature learning, classification/regression, retrieval, and image preprocessing.
- People with a machine/deep learning or pattern recognition background in medical image analysis are particularly encouraged to apply.
- Strong knowledge of programming (Python, C/C++, and Matlab) and a good sense of cooperation are highly desirable.
Dr. Liu’s MACHINE INTELLIGENCE IN BIOMEDICAL COMPUTING (MAGIC) lab is committed to developing innovative computational methods and tools for processing and analyzing medical imaging data. Our primary research interest lies in studying pathological changes in the human brain by creating innovative machine learning technologies for automated brain disease analysis. Current efforts are being directed to extending these works to harmonize/adapt multi-site data, integrate multiple modalities (e.g., MRI, PET, fMRI, DTI, CSF, and genetic data), and apply the resulting analysis paradigms to a number of important clinical studies, including Alzheimer’s disease, mild cognitive impairment (MCI), preclinical AD (e.g., subjective cognitive decline, SCD), major depressive disorders (MDD), and autism spectrum disorder (ASD).
If interested, please email your CV to Dr. Mingxia Liu: mxliu@med.unc.edu.