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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours 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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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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. 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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experimentation and training. Science of Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers of model performance at scale. As an early member of this fast-growing team
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., long-read spatial transcriptomics) across diverse organs, from the brain to the gonads, with state-of-the-art computational and deep-learning approaches. Projects may also include detailed molecular and
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, gender, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http
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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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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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initiation latency, movement speed, gait characteristics, postural control, motor variability, and other behavioural descriptors. ESSENTIAL REQUIREMENT PhD in machine learning, artificial intelligence
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experimentation and training. Science of Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers of model performance at scale. As an early member of this fast-growing team