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or accelerated acquisition and reconstruction algorithms will be highly valued. Instructions Interested candidates should apply via Interfolio link with their CV (including a full list of publications), a
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data management sufficient to create, transform and integrate data in a variety of resolutions and formats. Analysis will include running machine learning algorithms (e.g., Random Forest, CART) and
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available single-cell sequencing data generated from patient samples and mouse models, we will enhance and apply machine-learning based algorithms to deconvolute bulk tumor RNA-seq samples to distinct immune
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
. * Developing computational and algorithmic approaches to understanding the neurobiological mechanism of neurodisorders. * Interact cross-functionally: work with people across the team to find creative solutions
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
. * Developing computational and algorithmic approaches to understanding the neurobiological mechanism of neurodisorders. * Interact cross-functionally: work with people across the team to find creative solutions
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, physics, or a medical imaging related field. Experience with developing advanced pulse sequences or accelerated acquisition and reconstruction algorithms will be highly valued. Interested candidates should
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interdisciplinary teams to apply developed algorithms to real-world datasets and generate valuable biological insights. Perform integrative analyses of multidimensional datasets within the context of basic immunology
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. - Strong proficiency in machine learning, optimization algorithms, and computational modeling applied to construction systems. - Experience with designing and conducting experimental studies to evaluate
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/or machine learning/artificial intelligence algorithms. Projects may also include work focused on the analysis of spatial and geographic data and work extrapolating results to different spatial scales
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writing C++ and PyTorch. Training and debugging RL agents. Imitation Learning algorithms for robotics or autonomous vehicles. Prior work combining RL with human data or feedback. A track record of code