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to accelerate materials research and develop the next GW photovoltaic technology. We use automated research equipment to experimentally produce, characterize and optimize solar cells. In doing so, we collect a
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Your Job: In our team you will collaboratively work on: Collaborative development of semantic artifacts in materials science, in a scale- and method-bridging approach for crystallographic defects
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an existing simplified model Your Profile: You are studying engineering, industrial engineering, energy technology, physics or a comparable course of study. You are also characterized by: An interest in
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for licensing, spin-offs or cooperations for business and society. Your tasks in this exciting job in detail: You develop data- and technology-driven business models, carry out market and technology assessments
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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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: Very good performance in your Master`s degree in the fields of energy technology, mechanical engineering, materials science, civil engineering, industrial engineering, physics, resource management
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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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natural sciences or engineering and preferably a PhD and furthermore ideally already have good knowledge of science management and committee work You have already acquired basic knowledge and work
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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