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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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for candidates with completed scientific university education (Master's/Diploma) in food chemistry, chemistry or a related field sound knowledge and experience in the use of instrumental analytical methods such as
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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or very-good university degree in economics, business studies, agricultural sciences with a focus in economics, or related disciplines strong analytical and methodological skills with a focus on
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in economics, business studies, agricultural sciences with a focus in economics, or related disciplines Strong analytical and methodological skills with a focus on quantitative data analysis (e.g
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, HSPICE, or similar IC design tools Knowledge in at least one area as an advantage: deep learning hardware development memory technology CMOS technology Analytical and structured thinking paired with strong
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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independent research activities in the field of AI in education and learning analytics with the aim of obtaining a doctorate Teaching or supervising students' final theses Requirements Good to very good