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following computing skills will be considered an advantage: Natural Language Processing and LLMs; R; Python. Applicants must be fluent in English. Applicants who have completed their education outside
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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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(IRT) models in small samples. The ideal candidate has prior knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant
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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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before commencement. Certificates Academic work and R&D activities, as well as a list of these The applicant is fully responsible for submitting complete digital documentation before the closing deadline
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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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obtain binding data and high resolution crystal structures (Espeland LO, Georgiou C, Klein R, Bhukya H, Haug BE, Underhaug J, Mainkar PS, Brenk R. An Experimental Toolbox for Structure-Based Hit Discovery
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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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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