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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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) at the Technical University of Munich (TUM) is looking for a talented postdoctoral researcher (f/m/d) to deepen their expertise and interest in machine learning for medical image analysis and built their early
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on the design 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
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models. Your tasks: Research, development, and evaluation of Machine Learning and Deep Learning methods Prototype development Literature review Publication and presentation of scientific results in
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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
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the faculties of medicine and computer science at TUM, as well as the Munich Center for Machine Learning (MCML). It is a great place for interdisciplinary research between medicine and data science. We
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Management Technologies at TUM’s School of Engineering and Design is looking for a doctoral researcher (f/m/d) in the area of Collaborative Machine Learning for the Energy Transition. You are passionate about
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Candidates (f/m/d) in Machine Learning Applications to Sustainable Energy Management. You are passionate about applying cutting-edge information technology to solve the energy and climate crisis and would like
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journals. Close collaboration with team members and colleagues. Essential qualifications: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong
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this interdisciplinary project, we are looking for a strong candidate to contribute to the development of quantum algorithms and applications, focusing on quantum walks and quantum machine learning on graph structures