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integration technologies and transfers them into practical use. Fraunhofer ENAS accompanies customer projects from the idea to the design, technology development, or implementation using existing technologies
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of electrochemical and mechanical stress for All-Solid-State Batteries« in the Process and Production Engineering for Sustainable Energy Storage Systems department from 01.06.2024. What you will do • Developing
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communication networks. The department is currently involved in several research projects that analyze and optimize the environmental impact of information and communication technology, in particular optical
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for lithium-ion battery cells and fuel cells. Within the scope of your thesis, you will investigate deep learning-based defect detection approaches. To this end, we use roll-to-roll processes for the efficient
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processing, 3D scene reconstruction and analysis for digital and immersive media as well as medical technology and industrial applications. Become a part of our team and join us on our journey of research and
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be used in current and future key topics. Become a part of our team and join us on our journey of research and innovation! What you will do Develop software infrastructure for the deployment of machine
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on a journey of research and innovation! What you will do Research and development in the area of digital signal processing algorithms for coherent optical communication systems Implementation of DSP
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), electrodermal activity (EDA) and pupillometry. Become a part of our team and join us on our journey of research and innovation! What you will do Planning and conducting psychophysiological measurements
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Interest in long-term employment What you can expect Insights into exciting topics from industry and science Practical experience and consolidation of programming skills Flexible working hours If you are
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of research and innovation! What you will do Develop and test new deep learning architectures Analyze and preprocess different data types, mostly climate-, weather- and geo-data Conduct and document Deep