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As one of 76 institutes of the Fraunhofer-Gesellschaft, the leading organization for applied research in Europe, we show how to not only master crises, but grow from them. Pushing boundaries. Always
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right through to the tested prototype. The Data-based Methods team at Fraunhofer ENAS develops real-world applications using AI, machine learning, and computer vision. The main focus is on semiconductor
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to the table Enrolled Bachelor/Master student (m/f) with good grades in one of the following programs: Electric engineering, physics, cognitive science, applied mathematics, neuroscience or a related field. You
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. Therefore, within the scope of this master's thesis, different deep-learning approaches will be implemented and evaluated. What you will do Selection of suitable deep-learning approaches in the field
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Modern and excellently equipped workspace in central location Great and cooperative working atmosphere in an international team Opportunities to write a master's or bachelor´s thesis Flexible working
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team Opportunities to write a master's or bachelor´s thesis Flexible working hours The position is initially limited to 1 year. An extension is explicitly desired. The monthly working time is 80 hours
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for the implementation of standardized measurements (e.g. according to ITU-T L.1333 and L.1480) and life cycle assessments What you bring to the table Completed Master's degree in Electrical Engineering, Communications
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location Great and cooperative working atmosphere in an international team Opportunities to write a master's or bachelor's thesis Flexible working hours , up to 20 hours a week Opportunities to work from
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. Furthermore, you will support the supervision of master's and doctoral students. Further on, you will be involved in the further strategic development of this topic and in networking with project partners from
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Enrolled in Bachelor or Master program in the field of Computer Science, Information Technology, Industrial Engineering, etc. Interest in image processing, programming, and machine learning Programming