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the absolute forefront of observing and modeling two of Greenland’s largest glaciers -- Jakobshavn Isbræ and the Northeast Greenland Ice stream (NEGIS). You will use GNSS data on ice surface and bedrock
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Experience with Fortran, Python and Linux Shell Experience working with large datasets Preferred but not essential: Knowledge of wind farm parameterizations in mesoscale models Experience in the field of wave
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are represented as nonlinear flexible beams. Key stages and tasks will include: Developing and validating a one-way coupling model (prescribed wave-induced flow causing stem deflection) using existing measurements
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the development of γδ T cells and their importance in various diseases, such as cancer and inflammation. Technically, our work is heavily focused on flow cytometry and single-cell RNA-sequencing and as a successful
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. Your primary tasks will be to: Setting-up gas fermentation conversion systems. Cultivating and enriching mixed microbial consortia on carbon dioxide. Investigating electron flow to final metabolic
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bottlenecks in data and system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models
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to be cost competitive with other technologies, long lifetime of >5 years operation under high current density is desired. Operation conditions such as temperature, gas composition, current density and
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key