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extraction. So, from one side, there is a need for parsimonious machine learning approaches to classify, reconstruct and possibly segment 3D shapes. From another point of view, the aim of this PhD is to
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Engineering, etc.), expertise in cutting-edge AI and machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role
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multi-omics data integration and the project will provide opportunities to learn, develop, and apply machine learning and deep learning methods on genomics data. Requirements: excellent university and PhD
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of statistics, bioinformatics, and/or machine learning approaches are desirable but not required. This is a permanent position within the Nature Portfolio. The successful applicant will primarily support Nature
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thrombosis and lung injury in Sickle Cell Disease. The prospective candidate will have the opportunity to learn state-of-the-art techniques such as Multi-Photon-Excitation intravital microscopy of the lung and
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. Biomedical data science that combines methodology and implementation, in areas such as statistical modeling, natural language processing, bioimaging analytics, and machine learning/artificial intelligence
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candidate will be appointed to a full-time tenured position at the rank of Associate or Full Professor within the Faculty of Engineering. In addition to leading a world-class research program, they will teach
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(AI/Machine Learning (ML) expert for an academic investigational position. The Department of Data Science at the Dana-Farber Cancer Institute (DFCI) and the Department of Medical Oncology seek
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models