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the Norwegian educational system Experience with high-throughput sequencing omics data analysis Proficiency in programming with Python, R, or C++ Candidates without a master’s degree have until 31 August 2025
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. Documented experience with Bayesian spatiotemporal modelling, including experience with the INLA framework for Bayesian inference Documented experience with programming in either Python or R. Foreign completed
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the master's degree has been awarded. The candidate must have good knowledge in atmospheric dynamics. Proficiency in scientific coding and data analysis (e.g., Python, MATLAB, R, C++, FORTRAN) is required
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cycle processes, dynamics of oxygen and nutrient cycles, is required. Expertise in scientific scripting, programming, and data analysis (e.g., Python, Matlab, R) is required. Knowledge of climate
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knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant computing language. Experience with machine learning methods is a
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the link "Apply for this job". The following documentation must be uploaded electronically: Certificates with grades Master’s thesis References Academic work and R&D projects, as well as a list of these An
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, neuroscience, medicine, or similar Experience as clinical psychologist and with structured clinical assessments Expertise in statistical analysis using R or similar Experience with big data analysis with high
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techniques for screening plasmid libraries, such as phage display and yeast display is an advantage. Coding abilities in R/Python are an advantage. The candidate will work in a very ambitious interdisciplinary
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using Python, R, Matlab, Julia or similar is required. Knowledge of energy systems, energy system modelling or the European energy market will be an advantage. Understanding of atmospheric processes
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experience with designing experimental studies and analysing quantitative data. Proficiency in either R or Python. Favourable qualifications (not requirements, but give applicants an advantage): Relevant