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Geology or other related discipline Demonstrated expertise in machine learning and computer vision algorithms is necessary, with an emphasis on object tracking, optical flow and sensor fusion Knowledge
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of circularly polarized / chiral phonons in quantum paraelectric materials. Job description The postdoctoral researcher will develop machine-learned force fields trained on density functional theory (DFT) outputs
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for the position are expected to have a PhD in Chemistry, Material Science or Chemical Engineering. Experience in the fields of material synthesis as well as the physical and electrochemical characterization
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types. Profile A PhD in Computational Materials Physics or a related area is required. Experience with electronic structure calculations, including writing computer code, is essential. Familiarity with
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are required to have: A completed PhD degree. Experience in machine-learning methods. Some skill in at least one of these topics: Large data sets analysis Statistics and uncertainty analysis (probabilistic
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qualifications with a PhD in physics, electrical engineering, materials science or a related subject, and a background in magnetic thin films, nanostructures and spintronics. You should be motivated, proactive and
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modeling, interpretable and explainable machine learning, or hybrid modeling by combining process-based and data-driven approaches. Besides your own main project focus, you will contribute to the supervision
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conditions. In addition, there is a close collaboration with the EcoVision Lab of the Department of Mathematical Modeling and Machine Learning at the University of Zurich, which will facilitate the transfer
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(oral and written) and be willing to work and take over responsibility in a highly interactive international environment with other Postdocs and PhD students. Moreover, the candidate will also be involved