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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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, significance, and ethics into all course content, and deep commitment to engaging students beyond the classroom. FACULTY RESPONSIBILITIES Faculty members typically teach 24 semester hours per academic year
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Demonstrated research experience in machine learning, deep learning, medical image analysis, computer vision, biomedical data science, or a closely related area. Strong programming skills in Python and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
Demonstrated research experience in machine learning, deep learning, medical image analysis, computer vision, biomedical data science, or a closely related area. Strong programming skills in Python and
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foundations of medical deep learning. The project focuses on novel self-supervised objectives, information geometry, mitigating representation bias for rare pathological findings, and building next-generation
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25th September 2026 Languages English English English The Department of Marine Technology has a vacancy for a PhD Candidate in Deep Learning Enhanced FSI analysis of Modular Floating Structures PhD
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future. Fuelled by curiosity and a deep sense of responsibility, they provide invaluable contributions to research and teaching, thus enriching our society. Are you also inspired and driven by the desire
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23 Sep 2026 Job Information Organisation/Company WU Vienna University of Economics and Business Research Field Management sciences » Other Researcher Profile Recognised Researcher (R2) Positions PhD
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considered a strong asset. Experience with Deep Learning and Artificial Intelligence is considered a plus. Excellent proficiency in the English language is required, as well as good communication skills, both
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segmentation, normalization, and feature extraction in the time, frequency, and time-frequency domains. 4) Development and training of machine learning and deep learning models (such as SVM, Random Forests, CNN