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challenging, especially in dunes with complex topography. Your job In this 2-year postdoc position, you will use numerical modelling and machine learning techniques to increase the accuracy of coastal dune
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fluxes and acidification in global marine P cycling through a combination of field work in the North Atlantic Ocean, laboratory experiments and numerical modelling. A scientific cruise with our research
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postdoctoral researcher will contribute to the development of rigorous and repeatable methods for assessing whether, how, and to what extent these transition pathways are driving structural change. You will
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field. Demonstrated experience with numerical groundwater modelling, preferably including variable-density flow, solute transport, freshwater-saltwater processes, aquifer storage and recovery
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methods for assessing whether, how, and to what extent these transition pathways are driving structural change. You will: select and co-develop indicators and methods to assessing circular transition
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layer for gene expression control and numerous other processes in the cell. In this context, individual and combinatorial modifications guide spatiotemporal recruitment of regulatory factors to specific
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enhancement, manipulate ocean carbonate chemistry via electrochemical methods or alkalinity addition (enhanced rock weathering or via alkaline fluids), respectively. Both methods are beginning early pilot
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researcher who enjoys developing methods for complex biological data and working closely with experimental scientists. You meet the following criteria: a PhD, or a PhD close to completion, in machine learning
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teams that will directly test model predictions. Your qualities We are looking for a postdoctoral researcher who enjoys developing methods for complex biological data and working closely with experimental
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of clinically relevant targets for cancer immunotherapy. In this role, you will support the team by driving mass spectrometry-based method development and contributing your expertise to a range of proteogenomics