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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
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masters degree and subsequent Ph.D. degree in Computer Science, Mathematics, Physics Engineering or in a similar field. Alternatively an excellent masters degree with professional experience Very good
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building models Your Profile: Master studies in the field of of mechanical engineering, industrial engineering, process engineering, informatics and mathematics or similar Good knowledge of a programming
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of the Modelica building models Your Profile: Master studies in the field of of mechanical engineering, industrial engineering, process engineering, informatics and mathematics or similar Good knowledge of a
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the Institute of Physical Chemistry in Warsaw and Stuttgart University. Your Profile: You are an enthusiastic and motivated researcher You have a Masters degree and a PhD in physics, chemistry, engineering or
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-supervised learning in science Engage in national and international ML/DL communities, most importantly the Helmholtz Foundation Model Initiative Present research results at scientific meetings, conferences
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possible selection criteria Research on aggregation methods for raw material requirements at technology and system level Familiarization with the national energy system model ETHOS.NESTOR and, if necessary