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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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modelling and parameter estimation. This PhD project will be carried out in the Mathematical Systems Biology group at the Computational Biology Unit , a cross-departmental unit at UiB that works at
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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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collaboration. The successful candidate will be exposed to and trained in both low-level instrumental modeling, high-level component separation and cosmological parameter estimation, and high-performance
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. Study the effects of processing parameters, formulations, and ingredient functionality on product quality and process efficiency. Publish research results in peer-reviewed journals and disseminate
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precursors in the total exposure scenario, and the potential effects on physiology and reproductive parameters. In collaboration with partners at NINA, the results will be applied to model the possible effects
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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
data-driven modeling, parameter estimation, or model calibration Familiarity with high-performance computing or large-scale simulations Interest in close collaboration with experimental researchers
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statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model calibration Familiarity with high-performance computing or large-scale simulations
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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
programming skills Desired qualifications: Experience in statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model calibration Familiarity
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physics Solid programming skills Desired qualifications: Experience in statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model