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PhD in Computational Materials Physics or a related area is required. Experience with electronic structure calculations is essential. Familiarity with the use of machine-learning tools in materials
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setups, large-scale data acquisition and analysis, FPGA programming skills, and knowledge of Python and C++ are important assets. The project is highly interdisciplinary (microtechnology/electronics
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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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students. The institute has also published at most of the major machine learning, computer vision, and robotics conferences, including CVPR, NeurIPS, ICCV, ECCV, ICML, ICLR, IROS, AISTATS, as well as in
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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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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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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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background in microfabrication. Further, experience with experimental setups, large-scale data acquisition and analysis, FPGA programming skills, and knowledge of Python and C++ are important assets
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12 Feb 2025 Job Information Organisation/Company ETH Zürich Research Field Engineering » Electrical engineering Engineering » Materials engineering Physics » Other Researcher Profile Recognised
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conduct cutting-edge research smart contract development, and implementation for innovative applications and code analysis with the scope of on-chain data collection. The candidate would also be involved in