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Field
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balance of supervised investigation and work experience in a learning environment that will expose the participant to activities across the drug development process. We are seeking scientists from U.S
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transcriptomic data, that will be integrated with clinical metadata and whole-genome data for developing machine learning models to identify and predict patient factors driving toxicity response and sensitivity
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may include but are not limited to: algorithm and software development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning
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machine learning frameworks. Proficiency in writing clean, efficient, and well-documented code. Mathematics skills including linear algebra and partial differential equations. Ability to implement software
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evaluate machine learning approaches for predicting clinically successful drug targets. For this work, the postdoc will have access to a large high-performance compute cluster and to AbbVie's cutting-edge
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from sensors or other continuous data sources. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages
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pathology on computational, molecular, cellular, preclinical and translational levels. A spectrum of scientific methods includes state-of-the-art multi-omics approaches, machine learning and implementation
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research interests in one or more of the following subfields: scientific machine learning, optimization, deep learning, uncertainty quantification, (Bayesian) inverse problems, reduced order modeling, high
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from multiple disciplines and institutions. Required: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong background in machine learning / computer
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, bioinformatics, computer science, information science, biomedical engineering, or a related field. PhD must have been received within the last three years, 1 year of experience with machine learning, natural