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Computing (Hardware for Artificial Intelligence) Reference Number: IV-151/25 Are you excited about designing hardware that mimics biological intelligence with the aim to explore and understand how the brain
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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 Responsible for RTL design (VHDL, Verilog) of digital blocks and
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of ASIC/ FPGA SoC architecture and digital design Proficiency in hardware description languages such as System Verilog, Verilog, or VHDL Programming knowledge in Python and C Experience on frontend and
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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 innovative memory arrays for non-volatile memories Develop
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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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on solid state devices cooperate and actively work as a theorist with experimental partners improving quantum hardware design and implement optimization techniques for full-stack implementation of quantum
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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