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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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Kerr microscopy, transport, SQUID-VSM) as well as synchrotron x-ray techniques at international facilities. Profile You have outstanding qualifications with a PhD in physics, electrical engineering
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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
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, machine learning, modeling, and custom hardware, we test our solutions in various real-world projects with industry. Project background We foster a culture of continuous improvement, transparency, and no BS
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state-of-the-art device simulator. To accomplish the required work, we are looking for a post-doctoral fellow who will collaborate with the existing development team (two post-docs and four PhD students