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Field
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combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get
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also include technique development work aimed at combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a
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lignocellulosic biomass largely depend on the plant origin as well as the separation and extraction processes, which affect their durability and stability in the final products. Therefore, there is a great demand
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to be revalued. The project studies the processes through which value transformations of bio-waste are made. Examples of wastes that might be of interest are wastewater, stormwater or forest waste. The
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, or any other area relevant for the postdoc project. -Knowledge of and skills in processing, analyzing and publishing flux data from closed-chamber measurements, as evidenced through relevant first-authored
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related field, with a focus on computing or data science. Additional requirements are: Significant experience in bioinformatics, genomics, computing, machine-learning, or other data science area
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Worldwide R&D Projects. Previous experience in one or preferably more topics from these areas is considered a merit: -Multi-modal data processing for decision-making in natural environments -Terrain
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on personal abilities. All employees at MDU are expected to cooperate and treat colleagues and students with respect, take responsibility for the organisation and their own work duties and contribute to a
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efficient remediation technologies. At the same time, current recycling processes are greatly complicated by the presence of pollutants (chemical treatment products, heavy metals), which calls for further
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applied machine learning projects in, e.g., computer vision, in close collaboration with industry partners. The position is not connected to an existing project, so the postdoc fellow will either join an