173 parallel-computing-numerical-methods Fellowship positions at University of Oslo in Norway
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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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fluctuation power spectrum, building upon existing theoretical and computational frameworks. Position 2 – Commander4: Massively parallel joint Bayesian end-to-end analysis of past, present and future CMB
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with methods for causal inference Familiarity with administrative register data or other types of big data Familiarity with Stata, R, or other relevant computing languages Personal skills A collaborative
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Application Deadline 8 May 2025 - 23:00 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per Week 37.5 Is the job funded through the EU Research Framework Programme? Not funded by a EU
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Application Deadline 2 Apr 2025 - 23:00 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per Week 37.5 Is the job funded through the EU Research Framework Programme? Not funded by a EU
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candidate should have prior knowledge of quasi-experimental methods and, preferably, large data sources such as register data. The research fellow must take part in the faculty’s approved PhD program and is
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abstract mathematics and numerical methods to explain concrete physical phenomena. The successful applicant must have well-aligned research interests and background, and this should be described in
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: Prior experience with analysis of observational remote sensing data sets and/or numerical model output related to cloud properties is desirable. Strong computational skills, and ideally experience with
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computational methods. Knowledge of c++ programming language is particularly desirable Language requirement Fluent oral and written communication skills in English English requirements for applicants from outside