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discipline. At least 3 first author papers in disciplines related to neuroprosthetics. Published first author work in using machine learning algorithms for real time neural signal processing. Proficient in a
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to synthesize sequencing-based analysis results e.g.,Rshiny, d3, plotly, ggplot2. A solid understanding of statistics and experience in the implementation of machine learning and statistical inference algorithms
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and Machine Learning tools and algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability
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results e.g.,Rshiny, d3, plotly, ggplot2. A solid understanding of statistics and experience in the implementation of machine learning and statistical inference algorithms. Experience building web
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. Responsibilities* Design, implement, and evaluate LiDAR-based experiments in lab and real-world settings. Develop and test algorithms for object detection, tracking, and classification using LiDAR sensors. Help
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and digital signaling methods, data link protocols, error detection and correction, medium access control in broadcast networks, routing algorithms, internetworking, the Internet Protocol, connection
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Scalable Inference: Develop new algorithms for scalable uncertainty quantification (UQ) and Bayesian inference and apply them to challenging simulation problems. The goal is to produce robust, validated
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multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and their dynamics. Conducting literature searches, manuscript preparation, and
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, queues, and pointers in the implementation of algorithmic techniques including recursion, divide and conquer, and dynamic storage management. Graded ABCDE SWE 535 - Software Architecture and Design
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(e.g. transcriptomics, metabolomics) Knowledge of machine learning/deep learning algorithms Knowledge of systems biology approaches (e.g. genome-scale modeling) Proficient with R, Python or MATLAB Modes