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Field
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deep learning approaches on a wide variety of clinical data modalities, such as structured data, imaging and testing data, and free-text clinical notes. The researcher will have the opportunity to work
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, computer science, information science, data science, (bio)-statistics, (applied) mathematics, physics, or a related STEM field. Prior working experience with EHR data, machine learning, deep learning
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such as glaucoma, macular degeneration, and uveitis. Programming in Python and R languages with knowledge of Google Tensorflow, PyTorch, scikit-learn, and Keras or other related deep learning libraries
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upon future funding. Qualifications Required Education and Experience Appropriate PhD in a related field. Preferred Qualifications Experience with machine learning and deep neural network techniques
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profiles, to identify treatment response patterns, subtypes, and critical intervention windows that reduce Alzheimer's risk Disease (AD) risk. This position will involve applying machine learning, deep
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genomic data for reconstructing evolutionary patterns and processes that have shaped biological history across deep timescales. The ideal candidate will have a background in phylogenomics and bioinformatics
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ophthalmological, neuroimaging and behavioral data, and incorporate deep learning methods to facilitate biomarker discovery and enhance predictive power. As a postdoctoral associate you will join a multidisciplinary
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. Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to
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timings) affect the metabolome and proteome of rapeseed seeds. Your findings will serve as molecular fingerprints to support Deep Learning models for hybrid development. Whom we are looking for: An early
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view