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Field
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communication) Willingness to learn and confront new challenges Preferred Qualifications Doctoral research conducted in the area of machine learning for healthcare and related topics Deep knowledge of multi-modal
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. - Research Areas: Positions are focused around Deep Learning and Inverse Problem Regularization. Successful candidates will engage in diverse projects ranging from provincial to national levels, in
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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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genetics/genomics/omics, or 2) deep learning/AI. Most importantly, we value candidates who demonstrate both the ability and drive to rapidly learn and implement recent advances in AI. Create a Job Match for
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NASA. The researcher will utilize multi-decadal satellite imagery and deep learning techniques to analyze temporal trends in urban structure and their impacts on microclimate, focusing on extreme heat
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to an HPC infrastructure Containerized workflows and DSLs, e.g.: Nextflow, SnakeMake, Familiarity with deep learning libraries like TensorFlow and Pytorch would be a plus Competences Interdisciplinary
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” skills coupled with in silico data analysis and QC skills. Deep understanding of molecular protocols and capacity to “tear down” protocols, identify opportunities for improvement, and development
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to work on a project at the intersection of deep learning and computer security/privacy, under the direction of Dr. Michael Wu. The project seeks to investigate security and privacy problems in deep
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interpretable deep neural networks is required. Candidate must have published in top journal and conference at least one scientific paper in interpretable machine learning (not explanations of black boxes) among
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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