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Your Job: Analysis and integration of diverse biological data from the field of plant sciences (large scale genomic, transcriptomic, proteomic, metabolomic and/or single cell data) using cutting
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include: Researching, in particular, publicly available data on current and future materials for energy technologies Implementing the materials in our technology database and in our energy system model
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substitution options within the energy system Implementing the phased-out material flows and their substitution options in our technology database and in our energy system model ETHOS.FINE Deriving demand
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and develops a wide range of topics related to chemical hydrogen storage along the entire process chain. We place a particular emphasis on LOHC technology, addressing issues across different scales. Our
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machine learning-based software applications for materials science Develop code and utilize machine learning to support the automation of characterization and fabrication processes Ensure the integration
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addition to technical models allowing to assess technical scaling and an optimal operation. The optimal plant design is identified by considering the energy supply system, the choice of capture technology, the DAC
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data collection and metadata extraction protocols to enhance machine learning-based software applications for materials science. Your Profile: Master’s degree in Engineering, Computer Science, Physics
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/electronic engineering, computer science, computer engineering, physics, and related fields. For IC projects a strong electronics background, with experience in design and simulation of analog, digital
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Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Promotion Job description: Your Job: You will strengthen the data science and machine learning
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plant design is identified by considering the energy supply system, the choice of capture technology, the DAC-plant’s technical design, the DAC-plant’s operational concept and the positioning of the DAC