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sequencing, proteomics, and metabolomics; interpretation of datasets and clinical data using advanced statistical methods and machine learning algorithms to identify correlations between molecular alterations
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women and children’s health, nutritional sciences, population health and the molecular genetics of human disease. Our research links the causes of common health problems to life’s landmark stages
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Abrahao (NYU Shanghai) and João Sedoc (NYU Stern). Research Focus Areas Our research encompasses topics in DL and AI, including but not limited to: Deep Learning Algorithms and Paradigms Generative Models
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algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology, and expertise in computational methods, data analysis, software
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mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in
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algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology, and expertise in computational methods, data analysis, software
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-photonic computing architectures; Silicon-photonic network architectures Machine Learning Algorithms/Systems: Experience in design and use of ML algorithms; Experience in using ML for designing computing
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such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in English Desired qualifications: Experience with research on epidemiological
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applying interpretable AI / machine learning / deep learning / information-theoretic methods and algorithms in the context of multiscale biological networks, ranging from molecules (protein chemistry) to
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees