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biomedical image processing. Within the scope of machine learning and computer vision, there will be freedom to suggest your own research directions, and to become acquainted with new techniques and approaches
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
large data set types including RNAseq, DNAseq, RIPseq, and CLIPseq. Experience in gene expression analysis, alternative splicing analysis, machine learning, and motif analysis are preferred. Candidates
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FieldComputer science » OtherEducation LevelPhD or equivalent Skills/Qualifications CANDIDATE ’S PROFILE The candidate should possess a PhD in machine learning or computer vision and have a strong publication
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profile PhD in Computer Science, Data Science, Machine Learning, or a related discipline. Proven experience in computer vision (e.g. image processing, deep learning, object detection, segmentation) and
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP
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integration of AI-based vision and active machine learning to optimize the efficiency of the process. Writing publications and present research results from the project on conferences. Collaboration with
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: the School of Computing & Engineering, the National Centre for Computer Animation, The Media School and the School of Psychology. The breadth of subject areas in the Faculty means our cutting-edge research is
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Requirements Applicants must hold a PhD degree in Machine Learning, Artificial Intelligence, Computer Science, Statistics, or a closely related field. A strong research background and programming experience
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their work. Qualifications: PhD in Electrical and Computer Engineering, Biomedical Engineering, Mechanical Engineering or a related field. A combination of education and relevant experience from which
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to the application under CV/Resume. Required educations and experience Appropriate PhD in a related field. Preferred qualifications PhD, Computational Biology, Bioinformatics, Computer Science Training in statistics