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highly motivated doctoral student to join an ambitious project aimed at building machine and deep learning models to study the genetics of human disease. Funded as part of the Helmholtz AI program, the
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Your Job: Analysis of possible applications of CO2 capture and CO2 utilization in the energy system requires detailed techno-economic modelling of these technologies. A major focus of this doctoral
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under changing climate conditions 3.Assess land use change including wetland restoration in the Hamburg-Harburg region 4.Contribute model output to a user-friendly dissemination tool to provide actionable
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sites will not be located directly at the source of emissions, which will require transport infrastructure. As part of this work, you will model spatially resolved transformation paths for the energy
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isotope ratio mass spectrometry (IRMS) and gas chromatography coupled with mass spectroscopy (GC-MS), as well as other spectrometric methods Experimental and modelling analysis of relevant processes in
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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: Develop innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry
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supply system for providing electricity (and heat/cooling) for the DAC plant Detailed model for DAC operation with respect to fluctuating energy supply and climatic conditions Model for assessing
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combines microbial genomics, transcriptomics, and in vitro infection models to explore the adaptive trade-offs between drug resistance and virulence phenotypes. The position is funded for 3 years and
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pair distribution function (PDF) data for structure refinement and modelling of heterogeneous catalysts Publication of results in peer-reviewed scientific journals and presentation at conferences Your