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intelligence through hardware design. To this end, both the hardware foundation and the underlying hardware are continuously being developed. The design flow of these circuits and their (sub)systems is of
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Everyone is talking about artificial intelligence. But who is developing the necessary chips? We are, for example! Would you like to help drive the development of a new highly efficient AI hardware
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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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engineering a strong background in digital design, hardware description languages (e.g. Verilog, VHDL, SystemC), reconfigurable architectures (e.g. FPGA, CGRA) What we expect from you: above-average degree
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Requirements: excellent university degree (master or comparable) in computer engineering or electrical engineering a strong background in digital design, hardware description languages (e.g. Verilog, VHDL
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to co-design algorithms and circuits to develop efficient neuromorphic hardware, tailored to target tasks. In detail, you will: develop circuit-plausible training/inference algorithms and analyze in
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control, human-robot collaboration, and smart grids. For this reason, their design and deployment should be accompanied by a formal check of correct behaviour. The Research Training Group on Continuous
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innovation and active participation. For TUD diversity is an essential feature and a quality criterion of an excellent university. Accordingly, we welcome all applicants who would like to commit themselves