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22nd October 2025 Languages English English English Join NMBU’s PheNo project and advance AI-driven modelling in plant phenotyping. Postdoctoral fellow in Computational Plant Genetics and Digital
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to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field
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measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more
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of climate/weather model output are advantages. The LEAD AI mobility rules must be followed. Outgoing fellowships require an uninterrupted stay of minimum 12 months at an institution outside of Norway
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modelling, data-driven climate modelling, and working with large ensembles of climate/weather model output are advantages. The LEAD AI mobility rules must be followed. Outgoing fellowships require
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work upon. Suggested reading to explore this line of research further: Kitto, K., Hicks, B., & Buckingham Shum, S. (2023). Using causal models to bridge the divide between big data and educational theory
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well as experience in atmospheric dynamics or climate dynamics, basic shell scripting, and python/Matlab/R or similar languages. Experience with “traditional” climate modelling, data-driven climate modelling, and
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for the position. Preferred selection criteria Competence in the theory of superconductivity, quantum transport and/or quantum information/cavity physics will be advantageous. Competence in numerical modelling will
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quantum information/cavity physics will be advantageous. Competence in numerical modelling will be needed. Personal characteristics We are seeking a candidate who is independent, takes initiative and excels