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://www.uni-muenster.de/Geoinformatics/en/Studies/study_programs/PhD/application/index.html Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree
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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 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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can be found on our website . Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content IMPRS-ML – A Structured PhD
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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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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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research areas. New insights and synergetic effects resulting from collaboration between inherently different viewpoints of separate fields typically accompany this endeavour. Our task force on machine
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Degree PhD Course location Kaiserslautern Teaching language English Languages Discussions are held in English (100%). Programme duration 8 semesters Beginning Only for doctoral programmes: any time