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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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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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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | 16 days 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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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
Area of research: Laborkräfte Job description: Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic nodule fields (m/f/d) Background While some companies
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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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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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Python is required. Programming in C or C++ is a plus. Background in statistical genomics, longitudinal modeling, non-parametric statistics, machine learning and deep learning are preferred and encouraged
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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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responsibilities Design, implement and benchmark deep machine learning models for large-scale cancer datasets that include genomics, transcriptomics, epigenomics and imaging data Collaborate closely with
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, e.g. experience fitting Reinforcement Learning models or applying Agent Based Modelling to human behavioural data. You should have a deep understanding of the strengths and limitations