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science and energy technologies Basic knowledge of artificial intelligence and data analysis methods Programming skills, ideally in Python Independent and analytical way of working Reliable and thorough
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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PhD subjects, high interdisciplinary desire to learn and willingness to cooperate, very good verbal and written English communication skills. What we offer: Pioneering Research Environment: Shape
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the RTG. General Requirements: We are looking for first-class graduates with expertise in the RTG-addressed PhD subjects, high interdisciplinary desire to learn and willingness to cooperate, very good
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or very-good university degree in economics, business studies, agricultural sciences with a focus in economics, or related disciplines strong analytical and methodological skills with a focus on
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: excellent university degree (diploma, master's degree) in transport or related study programs with a solid basis in transport, data science, and/or data analytics; or equivalent practical experience
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for candidates with completed scientific university education (Master's/Diploma) in food chemistry, chemistry or a related field sound knowledge and experience in the use of instrumental analytical methods such as
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we