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Python is required. Programming in C or C++ is a plus. Background in statistical genomics, longitudinal modeling, non-parametric statistics, machine learning and deep learning are preferred and encouraged
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dedicated to discovering and refining the core mechanisms that will enable machines to learn continuously, make robust decisions in complex environments, and evolve autonomously. Key research directions
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utilizes a widely available diffraction-limited spinning disc confocal microscope (although not limited to this modality) for imaging. A single-step, machine-learning based approach is then applied
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in, but not limited to, the following areas are especially welcome: Reinforcement Learning Virtual Reality, Augmented Reality, Digital Avatars Embodied AI Natural Language Processing Human-Computer
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-of-the-art in SLAM, situational awareness, computer vision, machine learning, robotics, and related fields Developing and implementing innovative solutions, validated through real datasets and experiments
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facility, aligned with the evolving scientific and technological needs of the campus. Leverage cutting-edge computational tools, including machine learning and other emerging AI tools to accelerate
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at the Assistant, Associate or Professor level. We are currently recruiting candidates with expertise in data science, machine learning, computational or systems biology, and/or bioinformatics, with interest in
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R and experience with statistical analysis, machine learning methods, and high-performance computing. What we offer As well as the exciting opportunities this role presents, we also offer defined
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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machine learning techniques, predictive algorithms, and AI-powered tools to extract actionable insights to drive US Commercial strategies and tactics. Manage and mentor a team of data scientists (internal