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power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods
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to calculate your points for admission. Emphasis is also placed on your: background in algebraic or symplectic geometry or mathematical physics programming skills and experience with computer algebra packages
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addressing measurement quality issues related to respondent non-compliance in ecological momentary assessment, or exploring the use of machine learning techniques to aid the estimation of item response theory
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power electronics Machine learning Renewable energy systems Advanced statistics Language requirement: Good oral and written communication skills in English English requirements for applicants from outside
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or Machine Learning). The master thesis must be included in the application. Documented proficiency in English, please see requirements below. Documentation of skills in English language Strong communication
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, and AI chatbot chats; (b) quantitative content analysis; (c)text mining and machine learning methods; (d) survey design and public opinion research; (e) election studies; (f) the Norwegian political
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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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-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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power electronics Machine learning Renewable energy systems Advanced statistics Language requirement: Good oral and written communication skills in English English requirements for applicants from outside
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or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the