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these questions, we use a novel multi-omics approach that integrates high-throughput imaging and machine learning methods with CRISPR/Cas9 screens and saturation mutagenesis to answer central questions about the
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of artificial intelligence, including statistical/machine learning theory, optimization, and deep learning. Successful candidates must have a strong record of research and teaching and should complement
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning
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humanistic questions. We anticipate that the Postdoctoral Associates will teach one seminar per year, which is one section of SHUM 2750 Introduction to the Humanities in the spring term. As well, we expect
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to develop generative AI methods for nanoparticle drug delivery design, at the intersection of machine learning, explainability, and pharmaceutical nanotechnology. Job description We are looking for a
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Course Description: In Complexity of Clinical Care, the implications and practical application of the outputs of AI and Machine learning are discussed in class, and in select assigned readings. This class
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Development of innovative experimental model systems for mechanistic investigation and translational validation of microbiome-mediated processes Advanced AI and machine learning frameworks for integrative multi
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representation learning. You have strong programming skills, especially in Python, and preferably experience with PyTorch. You have a track record of publishing in top image processing / computer vision journals
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biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu/silverlab/ . Conduct independent research activities under the guidance of a faculty mentor in