1,544 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" positions in Germany
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! Be
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research of mobile and optical communication networks and systems, as well as in machine learning, computer vision, and the compression of digital video. Become a part of our team and join us on our journey
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the topic: 1. M Balaish, JLM Rupp, Widening the Range of Trackable Environmental and Health Pollutants for Li‐Garnet‐Based Sensors, Advanced Materials, 2021; https://doi.org/10.1002/adma.202100314 2. M
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skills in statistical analysis, data mining, data integration, machine learning, programming, backend or frontend development, and database design. Additional desirable skills include a sound understanding
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related field. Strong background in robotics, estimation, control, or machine learning. Strong proficiency in Python and/or C++ and experience with ROS/ROS2. Demonstrated research experience (e.g., Master’s
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Independent work ethic and desire to learn new methods and protocols A willingness to learn basic computer programming Work well with people from various cultures Ability to speak and write fluently in English
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! Be
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of different faiths and beliefs. Grounded in the Christian view of human life, the KU aims to create an academic and educational culture of responsibility. The research group Reliable Machine Learning at the KU
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, specifically methods that combine machine learning and optimization with physics-based simulation and/or physical constraints and translate these methods into impactful industrial applications. The position is
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systems with more than two energy levels (qudits) Scaling compilation approaches to utilize the processing power of parallel systems/supercomputers To learn more about our previous work, please check out