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meet the requirements for admission to the faculty's Doctoral Programme (Phd - NTNU ) Software skills in 2D/3D technical drawing Strong theoretical background in fluid mechanics, mathematics and physics
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for this position. Applicants should be proficient in R, Python, or equivalent statistical software. Some background knowledge in either (computational) Bayesian methods, or statistical learning for molecular data
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of Information Technology and Electrical Engineering. Knowledge of fundamentals of C++ programming. Competence in code optimization. Knowledge of hardware/software co-design principles, and computer architectures. Good
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computational skills (using R, modelling software, working on a remote linux-based server) and experience in analyzing Next Generation Sequencing data, including PCA, outlier analysis, GO-term enrichment analysis
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well as candidates with a background in machine learning methods. The PhD programme will straddle the boundaries between the field of wave modelling and the general field of machine learning, and we will set up a team
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Abandonment (P&A) Technology and within the framework of the ongoing industry sponsored research program SFI – Center for Subsurface Well Integrity, Plugging and Abandonment (SWIPA) https://www.sintef.no/en
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statistical analysis. You must meet the requirements for admission to the faculty's Doctoral Programme in Social Science . Good oral and written presentation skills in English. The appointment is to be made in
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. Addressing housing-related health risks in the USA, Vietnam, Turkey, and Ecuador, the project integrates community engagement, data science, and computational modeling. The key objectives of ComDisp
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computational modeling. The key objectives of ComDisp are: • Identifying and understanding housing, air quality, and respiratory health issues in each case study. • Linking climate change models to housing