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invites applicants for four PhD Fellowships in subsurface characterization within geosciences, reservoir engineering, molecular modelling, and machine learning at the Faculty of Science and Technology
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Intelligence (AI) and machine learning, digital sociology, AI and the sociology of knowledge, AI and expertise, and digitalization and criminology (digital criminology). A prerequisite for employment is that
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and the national infrastructure NorHTE, which is under establishment, to optimize synthetic steps (and routes) using machine learning in closed-loop (autonomous) optimizations and for parallel synthesis
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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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flexibility. We plan to use the departments laboratory for automated chemistry and the national infrastructure NorHTE, which is under establishment, to optimize synthetic steps (and routes) using machine
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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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requirement Experience with data analysis and machine learning models is an advantage Experience from molecular modelling or molecular dynamics simulations is an advantage Applicants must be able to work
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employment that the master's degree has been awarded. Experience from protein bioinformatics is a requirement Good programming skills are a requirement Experience with data analysis and machine learning models
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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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Experience with high-throughput sequencing omics data analysis Proficiency in programming with Python, R, or C++ Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular