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Field
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interest in methods development Experience in one or more of the following areas: algorithms development, transcriptome analysis, RNA modifications, statistics, machine learning, long read RNA-Sequencing
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the potential to impact on protected groups and take appropriate action. Desirable Skills: Experience with machine learning or natural language processing. Knowledge of econometric methods for policy evaluation
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-compliance in ecological momentary assessment, or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models in small samples. The ideal candidate has prior
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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data
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statistical models aimed at substantiating the explanatory account the project will offer. Co-authoring papers for submission to top academic journals based on the research and contributing to the writing of a
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in writing grant applications and working with machine learning approaches such as MaxEnt, random forest, neural network. Experience using Geographical Information Systems and ecological niche modeling
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focus of the position is on application and advancement of modern artificial intelligence (AI) methods in drug discovery and development. An ideal candidate will have strong background in machine learning
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mitigation, dynamic data integration, validation, and interoperability. The research will involve applying various techniques, such as representation learning methods and transformer-based models, to tackle
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at CERI under supervision of Dr. Goebel. and should contribute broadly to induced seismicity research. Specific research topics include seismicity analysis, machine learning, reservoir modeling, geothermal
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, development of complex data visualizations, and demonstrated skill in one or more of the following areas: machine learning, statistical analysis, natural language processing and text analysis; experience