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the unique nature of clinical medical data with low disease prevalence and difficulty obtaining ground truth data. Major regulatory science gaps include lack of methods for AI algorithm training with limited
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computational tools and algorithms, including emerging AI/ML based approaches, for genome annotation, comparative genomics, and multi-omics analyses, with particular attention to the challenges of complex
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development of transparent, closed-loop control system for individualized diuretic closing including the validation and advancement of machine-learning and control algorithms, building production-oriented
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. In this position, your primary task will be to lead the development of algorithms, software, and hardware to extend the current HAUCS framework. This includes developing the sensors, sensing robotic
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is interested in understanding the neural and algorithmic basis of sensory-guided behaviors in terrestrial animals. We have developed behavioral tasks in mice using stimuli and situations
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diseases and their impact on interstate and international trade and community health. They will learn surveillance procedures, diagnostic testing methodologies and algorithms, serological diagnostic methods
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for characterization of CT images. Machine and Deep learning: Develop and implement machine learning and deep learning algorithms to built detection and prediction models for CT images Performance Evaluation: Conduct
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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application to lineage tracing Algorithms for characterizing structural alterations in bulk and single cell whole-genome data Mutational signature analysis for cancer/brain samples Analysis of repetitive
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees