25 molecular-modeling-or-molecular-dynamic-simulation Postdoctoral positions in Norway
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Ratio arkitekter 15th August 2025 Languages English English English Postdoctoral/Researcher position in Molecular and Systems Neuroscience through the NORPOD program Apply for this job See
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properly. Please turn on JavaScript in your browser and try again. Ratio arkitekter 15th August 2025 Languages English English English Postdoctoral/Researcher position in Molecular and Systems Neuroscience
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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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are invited for a 3-year postdoctoral position financed by an Eranet program to investigate the impact of childhood adversities on adulthood psychopathology in a rodent model using in vivo electrophysiology
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dynamics or climate dynamics, basic shell scripting, and python/Matlab/R or similar languages. Experience with “traditional” climate modelling, data-driven climate modelling, and working with large ensembles
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climate change are far-reaching, particularly when it comes to identifying and interpreting trends in regional-to-local scale signals and extreme events. Large ensembles of climate simulations are a key
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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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on developing and implementing AI models and methods for improved segmentation and classification of laser data. The position is for a duration of 3 years and is located at NIBIO’s headquarters in Ås, 30 km south
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systems to adapt and learn from data - identifying which measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and
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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