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An exciting postdoctoral position is available in the exciting field of mathematics of deep learning, under the joint supervision of Prof. Alex Cloninger and Prof. Gal Mishne at UC San Diego. This NSF-funded
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | about 1 month ago
increasingly utilizes big data, satellite imagery, register data, and advanced methods such as deep learning and neural networks to address major societal challenges related to spatial inequalities and
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engineering, neuroscience, computational biology, or a closely related field ï‚· Strong oral and written communication skills ï‚· Demonstrated motivation, initiative, and attention to detail ï‚· Deep interest
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the areas: AI, deep neural networks, machine learning, applied topology, probability, statistics, signal processing. About the School The School has an exceptionally strong research presence across
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, band selection Heterogeneous network architectures, including terrestrial and non-terrestrial networks Deep learning for wireless communication problems, particularly in areas such as spectrum management
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Free probability theory High-dimensional probability, concentration and functional inequalities Mathematical aspects of machine learning and deep neural networks Free Probability aspects of Quantum
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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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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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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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: 12 September 2025 Apply now Are you a data scientist interested in designing and implementing process-informed machine learning and uncertainties quantification methods? Join us as a postdoc and work