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Field
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: Artificial Intelligence, including but not limited to Machine Learning, Responsible AI, and Computer Vision; Theoretical Computer Science, including Algorithms, Algorithmic Fairness, Game Theory, and Learning
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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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limitations or potential issues with datasets and data types that may impact performance from machine learning algorithms. Re-trains models as needed to address new challenges or novel goals. Aids investigators
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in artificial intelligence (AI) for settings involving multiple interacting decision-makers---whether autonomous AI agents, humans, or a combination of both. Applications include mixed-autonomy
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issues or areas of critical data examination. Works with statistics on defining and documenting programming endpoint algorithms across a study, drug program and/or contributing to TA level algorithms
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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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courses on data mining, algorithmic bias, data ethics, and critical data studies. Intermediate and upper level courses could include modeling and simulation, data analytics for spatial analysis, geographic
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and software. In addition, they will work on algorithm development, optimization, programming, and computer science research. Hourly Salary Range: $37.60 – $78.35 JOB QUALIFICATIONS Qualifications All
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machine learning algorithms for application to sonar and underwater acoustics, as well as the accompanying data analysis to effectively characterize their performance in the Advanced Technology Laboratory
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patients and cancer-free individuals, and will integrate these data alongside other data modalities (e.g., patient outcomes, functional genomics) to enable new clinically relevant discoveries across multiple