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Field
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Atlassian products (JIRA, Bitbucket, Confluence) Experience ensuring reliable data collection; monitoring and auditing integrity of data throughout its lifecycle Experience employing algorithms to model and
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algorithms to model and estimate data to handle missing/corrupt values; time series modeling and prediction Data technologies and data engineering – distributed systems, MQTT, ETL with databases with large
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• Flexibility to learn new technologies, APIs, and SDKs by reading documentation • Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc
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learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc. • Experience with common data science toolkits, such as R, Weka, NumPy, MATLAB, etc. Excellence in at least one of
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, APIs, and SDKs by reading documentation • Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc. • Experience with common data
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algorithms Improve performance through tuning the GPU code Document, test, and debug revisions of the software for release Coordinate conversion of LM states of cells into coarse-grained atomistic
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for distributed AI training, agentic AI with modeling and simulation, and end-to-end workflow monitoring, profiling, and optimization. Working with quantum simulation tools, including NVIDIA CUDA-Q, to enable
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range of areas including but not limited to Artificial Intelligence, Machine Learning, Data Science, Security and Privacy, Parallel and Distributed Computing, Deep Learning, Internet of Things (IoT), and
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geometries and advancing to multimodal damage assessment capabilities. The group conducts cutting edge research and publishes on novel ML breakthrough algorithms for large scale geospatial application
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algorithms Familiar with the hardware and software environments of high-performance computing systems The ability to perform independent innovative research under minimal supervision, including the design