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Apply machine learning techniques for data analysis and time-series forecasting Collaborate in a multidisciplinary team to accelerate functional thin film development Your work will be part of a larger
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of academic output. Presenting the project internally and externally, and preparing material for such presentations. Profile Mandatory Requirements: You have: Demonstrable experience with machine learning tools
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responsibility in a highly interactive international environment with other Postdocs and PhD students. Moreover, the candidate will also be involved in project management and grant acquisition. The position is
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team members Perform accelerated optical degradation tests of transparent conductive materials Apply machine learning techniques for data analysis and time-series forecasting Collaborate in a
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progress Profile Required PhD in Computer Science with the focus on AI Proficiency in python programming Strong expertise in machine learning and deep learning frameworks (especially PyTorch) Demonstrated
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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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2025. Profile A PhD (or equivalent doctorate) in Business Administration, Management, or a related field (e.g., Psychology, Sociology, Engineering, or Economics). A strong publication record (or
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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