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- NTNU - Norwegian University of Science and Technology
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- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
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the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The project PI and team are also in close collaboration
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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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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
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, or C++ Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling
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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
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variables, fixed effects for panel data, matching estimators, or machine learning) or other advanced statistical modelling.- Advanced programming skills in Stata, R, Python or a similar software.- Strong
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the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The project PI and team are also in close collaboration
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foundation for theory-guided catalyst design e. g. by machine learning approaches. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good quality within
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application”. If you do not already have educational competence that meets the requirements for a position as associate professor in Norway, NTNU will arrange for you to acquire such competence during
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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