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role in the global carbon cycle by transferring carbon from the surface ocean to the deep sea through the formation, sinking, and remineralization of organic particles. Despite its importance, our
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bodies down to the bottom of the deep sea. The Aquatic Life Foundation Project (AqQua ) will, for the first time, combine billions of images acquired with a variety of devices across the globe for large
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field such as computer science, bioinformatics, mathematics, computational life sciences, or related. Profound knowledge in machine learning, preferably deep learning for image data. Experience in
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interdisciplinary thinking • High level of initiative and commitment • Ability to work in a team and communication skills • Good knowledge of Ansys Fluent • Experience with deep learning methods and packages
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environment (e.g. cleanroom, laboratory Deep knowledge of solid state physics and/or quantum information Experience with microfabrication and/or operating and calibrating quantum systems Experience with
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. Topic / Area: The positions are flexible in terms of research direction within 3D vision and graphics with a heavy focus on cutting-edge deep learning-based techniques. We are particularly interested in
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(e.g. via machine learning) to qualitative analyses (e.g. via interviews) to support ambitious policies for climate and energy transitions. This position Green hydrogen is key to decarbonizing many hard
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focus on deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning
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(ML4Earth). AI methods, and especially machine learning (ML) with deep neural networks have replaced traditional data analysis methods in recent years. The Technical University of Munich (TUM), together
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background in a technical field such as computer science, bioinformatics, mathematics, computational life sciences or related. Profound knowledge in machine learning, preferably deep learning for image data. A