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open PhD positions throughout the year. Learn more about PhD opportunities at the Hertie School and the relevant application deadlines here . Further information on the application procedure can be found
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2019, unites top PhD students in all areas of data-driven research and technology, including scalable storage, stream processing, data cleaning, machine learning and deep learning, text processing, data
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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 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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ranges from core areas of computer science and electronics over medical applications to societal aspects of AI. SECAI’s main research focus areas are: Composite AI: How can machine learning and symbolic AI
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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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, medical informatics, databases, data mining, machine learning, applied mathematics, biomedical modelling and analysis of complex networks. Joint data science projects between the different partners