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Field
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analysis Machine learning and retrieval-augmented AI models for biomarker prioritization and decision support ·Work closely with cross-functional team members to develop hypotheses, interpret data, and
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machine learning. The successful candidate will develop and apply methods that integrate multimodal molecular and clinical data (genomic, epigenomic, transcriptomic) across serial patient timepoints
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Center, and will involve close interaction with researchers in machine learning, statistics and data science at UiT, as well as collaborators at Simula Research Laboratory and other partner institutions
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many areas of applied and theoretical statistics and data science, and is heavily involved in research at the crossing of statistics and machine learning. The focus of this postdoctoral fellowship is to
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application of machine learning and AI methods to large-scale, longitudinal, routinely collected eRegistry data. The successful candidate will collaborate with researchers, PhD candidates, postdoctoral fellows
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in machine learning and/or computer vision as applied to robotics. - Strong publication record and demonstrated research independence. The referenced salary range is based on Johns Hopkins University's
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techniques. You will have the opportunity to participate in various projects utilizing artificial intelligence (AI) and machine learning (ML) to develop applications that optimize combat casualty care
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data integration and analysis Integrate phylogenomic and functional data using machine-learning approaches for candidate gene prioritisation Contribute to software and web-tool development Present
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Postdoctoral Research Fellow - Michor Lab Dana-Farber Cancer Institute Boston, MA Full Time The lab of Prof. Franziska Michor, PhD at the Dana-Farber Cancer Institute (Department of Data Science
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the Lotfollahi Lab – leaders in generative AI and foundation models for spatial and single-cell genomics – you will develop and apply state-of-the-art machine learning approaches to large-scale spatial genomics