278 algorithm-"Multiple"-"Prof"-"NTNU---Norwegian-University-of-Science-and-Technology" positions in United States
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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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algorithms from concept to implementation; and performing systems integration and testing for technology maturation. Minimum Qualifications: 1. Ph.D. in relevant technical field 2. 6 years experience in
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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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responsibilities – position supports the Cancer Biology Program, with the expectation it will interface with multiple research teams with varied research interests, projects and timelines Qualifications Requirements
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. This includes: • Developing targeted campaigns that highlight faculty research, student success, and innovative programs—key metrics in ranking algorithms such as U.S. News & World Report, QS World University
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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