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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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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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of models in existing simulation software conducting numerical studies, also on HPC systems Further specific tasks can be tailored to the attitude and interests of the PhD students/postdocs. Requirements
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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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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 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 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 writing Verilog/ VHDL code for AI blocks Perform
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screening (Ulrike Haug), prevention and implementation science (Hajo Zeeb, Daniela Fuhr), biostatistics, machine learning, data science and research data management, and causal inference methods (Iris Pigeot