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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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are also expected. Profile PhD in Data Science, Computer Science, Mechatronics, Remote Sensing, Engineering Geology or other related discipline Demonstrated expertise in machine learning and computer vision
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, ICLR, NeurIPS, CoRL, ICRA, AAAI, AAMAS or similar) A collaborative mindset, with the ability to work effectively within a multidisciplinary team (Optional) Experience with large-scale machine learning
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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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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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with machine learning algorithms to predict areas at risk for reduced forest vitality and tree species decline. Key Responsibilities: Design multimodal data fusion techniques to integrate multispectral