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industries. Overarching top topics at Fraunhofer ITWM are Machine Learning as well as Artificial Intelligence and Renewable Energies or Sustainability. In addition, next generation computing and quantum
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movement by high-resolution fluorescence microscopy and/or magnetic/optical tweezers, (iv) quantitative image processing, data analysis and modeling. The project is funded by the MARIE SKŁODOWSKA CURIE
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movement by high-resolution fluorescence microscopy and/or magnetic/optical tweezers, (iv) quantitative image processing, data analysis and modeling. The project is funded by the MARIE SKŁODOWSKA CURIE
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or a comparable course of study Good Python and/or Java programming skills Machine learning knowledge and experience Experience with Static Analysis is recommended Good language skills in German and/or
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existing technologies, 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
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and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods
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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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and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods
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existing technologies, 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
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In manufacturing, a wide variety of use cases exist where Deep Learning (DL) and Machine Learning (ML) are successfully applied. Examples of use cases include the production of rockets, stem cells