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intelligence, data science, medical physics, neuroimaging, bioengineering, or related disciplines, accompanied by accredited training in machine learning, deep learning, or medical image analysis. Experience: A
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archaeological signatures (e.g., micro-relief, edge structures, etc.) – Design and implementation of new deep learning architectures (both supervised and unsupervised/few-shot, 2D and 3D) for an efficient and
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earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc. Documented experience in machine learning, in particular deep generative
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 19 hours ago
cleaning, denoising, and prediction, including approaches based on statistical machine learning, deep learning, and Transformer architectures. Perform signal analysis in both the Fourier (frequency) domain
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-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and functional evolution
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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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the traditional custodians of the land, sea and waters of the areas upon which we live and work. We recognise their valuable contributions and deep connection to country and pay respect to Elders past
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them using state-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and
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the position of: Lecturer for the Module M06 'Advanced Deep Learning' 10 ECTS (18%) Start date: August 1st, 2027 Module completion: each autumn semester (beginning of August to end of January) Description
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Required knowledge Skills Focus proficiency in one programming language (e.g. Matlab, R, Python), machine learning / deep learning / data science skills, basic understanding of cell and development