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techniques—including vision-language architectures (e.g., CLIP, BLIP), fine-tuning large language models for clinical NLP, and self-supervised contrastive learning—the models will learn to effectively combine
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 8 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