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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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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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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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international research environment covering a wide variety of research areas, such as algorithms and data structures, machine learning, computer graphics and vision, database systems, artificial intelligence
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 22 hours ago
Degree Doctor of Philosophy (PhD) in Environmental Sciences Course location Cottbus Teaching language English Languages All courses are taught in English. The dissertation is written in English, and
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take place monthly. A lecture series on theoretical and experimental neuroscience as well as machine learning is addressed primarily to doctoral students. Lectures are held by principal investigators
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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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Max Planck Institute for Human Cognitive and Brain Sciences • | Leipzig, Sachsen | Germany | 2 days ago
the IMPRS CoNI is English, German language skills are not mandatory. For PhD applicants, a minimum score required for a TOEFL paper-based test is 550. The minimum score for the TOEFL computer-based test is
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always indicate child-rearing periods of the nominee when filling in our nomination form. Mothers can claim two extra years for each child born after their PhD plus any further verifiable parenting periods
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for improved understanding of structural and kinetic processes in electrolytes; and machine learning concepts for improved analysis of experimental and simulated data. Material Synthesis Within this research