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into account various parameters. To this end, a concept is developed, various mathematical models and Machine Learning algorithms are selected and then tested and evaluated within a company environment. What you
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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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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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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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) Mathematical Modeling, Optimization, and Simulation Classical Image Processing and Machine/Deep Learning Probalistic Sensor Data Processing ( Kalman Filter, etc.) What you can expect A dynamic work environment
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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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Technology, Mechatronics, Computer Science, or a similar field? Do you have experience using Python and speak English? Do you have basic knowledge in artificial intelligence and machine learning, along with a
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methods (such as Machine Learning, Metric Learning, Reinforcement Learning, Graph Representation Learning, Generative Models, Domain Adaptation, etc.) for Design Automation applications. To this end, we