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the experiment and analyzing the results in order to answer the research question. It is likely, however, that the results can also be used to answer other questions and therefore existing results are an unused
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to the FAIR-ification of data, establishing systematic data collection and metadata extraction protocols to enhance machine learning-based software applications for materials science. Your Profile: Master’s
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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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H2 profiles in collaboration with our modeling department Your Profile: MSc in meteorology, physics, chemistry, environmental sciences or a related field with good final grade (German system equivalent
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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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Your Job: You will strengthen the data science and machine learning activities of the IAS-9 with exciting new topics. You will work in a multidisciplinary team of enthusiastic data scientists
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: https://www.fz-juelich.de/profile/frunzke_j Your Profile: Highly motivated and excellent candidates with a Master of Science degree in biology, biochemistry or related subjects are strongly encouraged
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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
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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