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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
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TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally oriented
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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 Stay engaged with the latest research, experimenting with cutting
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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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Bioinformatics, Computational Biology, Computer Science, Biomedical Engineering, Computer Engineering, Genetics/Genomics or related field experience with ‘omics platform output experience with biological datasets
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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 Responsible for RTL design (VHDL, Verilog) of digital blocks 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 Responsible for writing Verilog/ VHDL code for AI blocks Perform
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environment; materials; mathematics in sciences, engineering, and economics; humans and technology. KIT stands for a research-oriented and student-oriented teaching and learning culture. After having completed
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analysis of large data sets, statistical modeling, and knowledge of at least one programming language (e. g.: R, Python and/or Julia) are required. Experience in machine learning and image recognition