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of multi-fidelity and active learning strategies for molecular systems. The candidate will collaborate in an international research team on related research questions in machine learning, uncertainty
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– from the modeling of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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international collaboration with mentors from the USA, Asia, and Europe. TUD and the RTG embody a university culture that is characterized by cosmopolitanism, mutual appreciation, thriving innovation and active
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international collaboration with mentors from the USA, Asia, and Europe. TUD and the RTG embody a university culture that is characterized by cosmopolitanism, mutual appreciation, thriving innovation and active
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digital age” drives collaboration with academic and nonacademic partner institutions in the Hamburg Metropolitan Region and around the world. We would like to invite you to be part of our community to work
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laboratories, a highly collaborative international environment and membership of the Cluster of Excellence "Machine Learning: New Perspectives for Science", which is funded by the DFG and is located
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chemistry is required, as well as a willingness to learn and apply new methods. Capability of team work is essential, initiative and excellent communication skills Willingness to collaborate in
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studies using 2D and 3D cell culture models, e.g. human organoids Apply and develop cutting-edge techniques in virology, cell biology, molecular biology Establish and maintain collaborative projects within
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microstructures along the entire process chain using machine‑learning (ML) techniques and validate soft‑sensor outputs against laboratory reference measurements Perform systematic laboratory flotation experiments