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reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
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or infrastructure. This is what makes our daily work so meaningful and exciting. The Division of Computational Genomics and Systems Genetics is seeking from October 2025 a PhD Student in Deep Learning for Rare
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, ideally in Python, with demonstrated experience in applying them in complex research or development projects Basic knowledge in Machine Learning, ideally supported by initial hands-on experience Experience
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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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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 TV-L, part
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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