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Description At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13
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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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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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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
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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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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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13.02.2025, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Computer Engineering at TUM has open positions for a doctoral researcher (TV-L E13 100%, 3-4 years