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preparation Deriving insights for film and device fabrication based on the above described workflow Your Profile: Masters degree in physics, materials science, physical chemistry, electrical engineering and
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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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for self-supervised learning in science Engage in national and international ML/DL communities, most importantly the Helmholtz Foundation Model Initiative Present research results at scientific meeting
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that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with: Opportunity to work on highly relevant engineering challenges with state
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Your Job: However, one of the main hurdles related to AEMWE technology is to develop low-cost and robust catalysts that can withstand the harsh reaction environment without compromising
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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 modelling pipeline Your Profile: A university degree (Master) with subsequent PhD in Applied Mathematics, Mathematical Biology, Computational Physics/Engineering or related fields with strong relation
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and efficiency Your Profile: Very good academic record and currently enrolled in a Master’s program in engineering (energy technology, mechanical engineering, industrial engineering) or a related field
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contribute to regular progress reports Your Profile: Completed Apprenticeship in Chemistry or Materials Science or comparable Background knowledge and interest in electrochemistry and organic/polymer synthesis