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of a prototype software module that automatically generates feedback in the defined format from the selected metrics and tests. Quantitative and qualitative evaluation of the feedback process by
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members in one of Europe’s largest interdisciplinary research centers and help us to shape change! The Faculty of Agricultural, Nutritional and Engineering Sciences of the Rheinische Friedrich-Wilhelms
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years of experience with extensive expertise in microelectronics and microsystems technology. Our wide range of technologies spans from advanced semiconductor processes and MEMS technologies to innovative
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power, energy, space) to develop systems that reliably capture and process input data such as images and speech and provide comprehensible feedback. This topic opens a wide range of possible applications
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data to modeling in digital representations to the final visualization and human-machine interaction. We develop modern AI solutions in the areas of 2D/3D image and video understanding, computer vision
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Field of study: computer science, mathematics, software design, software engineering, technical computer science or comparable. Machine Learning (ML) models are reaching a maturity level that allows
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or Mechanical Engineering – strong background in machine learning, deep learning and / or computer vision – good programming skills in Python (and C++) – basic knowledge of optics including concepts like PSF, MTF
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Electrical Engineering / Mechatronics / Automation Engineering / Computer Science or related fields Interest in battery technologies and electromobility Basic knowledge of electrochemistry or willingness
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transfer. Its current five-year scientific programme, Molecules to Ecosystems (2022–2026), aims to deepen our understanding of life — from molecular mechanisms to complex ecosystems — with a strong emphasis
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novel machine learning-guided approaches. The position is located at TUM Campus Heilbronn. Your qualifications Strong background in computer science, AI, or related areas or similar fields. Solid