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Field
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learning algorithms into professional software with an intuitive user interface, incorporating feedback from CHWs through iterative design and evaluation cycles. The selected candidate will be part of a
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encouraged to adopt a creative approach to problem-solving, exploring various deep learning techniques. Verification of these models and algorithms will be conducted using benchmark datasets and real-world
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, and experience in the algorithms used to analyze these datasets. The appointee will ultimately create an independent research effort with dedicated extramural funding that complements existing strengths
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Preferred Qualifications: Experience with one or more of the following: Building novel 3D and super-resolution ultrasound systems. Developing deep learning algorithms for 3D biological data. Designing and
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calculations, reactive empirical force fields, chemical dynamics, deep learning and numerical algorithms, data analysis, experimental characterization and imaging. Our research has involved methodology and
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and marine industry to a more efficient, safe, and eco-friendly future. Some of the current research topics focus on advancement of reinforcement learning algorithm for vortical flow control and sensing.
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and marine industry to a more efficient, safe, and eco-friendly future. Some of the current research topics focus on advancement of reinforcement learning algorithm for vortical flow control and sensing.
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properties, or multiscale modeling will be preferred. The appointee will be expected to (i) develop and implement machine learning algorithms for materials design and discovery; (ii) collaborate with
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-Gurevich Probability, random walks, random graphs, probabalistic algorithms Genadi Levin Complex and one-dimensional dynamics, non-linear phenomena Noam Lifshitz Extremal combinatorics, Additive
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reconstruction algorithms that incorporate multiply-beam coherent scattering imaging in a grazing incidence geometry to improve the spatial resolution to ultimately demonstrate the utility of the novel coherent