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of embedded machine learning, neuromorphic hardware and deep learning accelerators. Want to get more information? Click here. What you will do The complete design and implementation of analog circuits including
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of embedded machine learning, neuromorphic hardware and deep learning accelerators. Want to get more information? Click here. What you will do Design analog and mixed-signal circuits, such as data converters
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of IC design with a focus on new architectures of CMOS backplanes. What you will do Developing, implementing and experimentally testing concepts for CMOS backplanes for OLED microdisplays Designing analog
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scenarios and strategies for its successful implementation. Using existing ICE-2 energy system models, you will address questions such as: How do installation and retrofitting times impact the restructuring
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reuse of energy systems analysis processes. In line with FAIR and Linked Open Data principles, you will design interfaces that enable the smooth processing of big data in the context of scientific, AI
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the perspective of energy infrastructure. Using the existing energy system models of ICE-2, you will develop scenarios to analyze the effects on the German energy system and its robust design in a European context
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. Cryo-EM has become a very powerful method to visualize ice-embedded samples including purified proteins at close to atomic resolution. Typically, single-particle cryo-EM as well as cryo-ET are based
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design (sub)components of digital circuits. A focus will be on the use and development of automated and innovative design tools. You'll use access to cutting-edge, CMOS-based IC technologies for your
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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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to their individual needs. The language of instruction at BAGSS is English. You can write and defend your thesis in English or in German. The programme is designed to be completed in three years. During this time