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-dimensional probability, concentration and functional inequalities ? Mathematical aspects of machine learning and deep neural networks ? Free Probability aspects of Quantum Information Theory. While excellent
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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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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
management) Your Profile PhD in marine microbial ecology with a focus on molecular ecology or ecological genomics or closely related topics - ideally with a deep-ocean focus Professional experience as a
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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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Electrical Engineering, Computer Science, or a related field Strong background in speech processing, signal processing or machine learning Proficiency in Python and deep learning frameworks Experience with far
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
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Electrical Engineering, Computer Science, or a related field Strong background in speech processing, signal processing or machine learning Proficiency in Python and deep learning frameworks Experience with far
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robust models – and for clinicians, whose goal is to determine when to trust the models. We therefore seek candidates who have strong technical background in working with large-scale deep learning models
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the intersection of machine learning and genomics. The project involves the development and application of advanced machine learning and deep learning techniques to understand the sequence-function relationships