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or similar tools) Initial experience with machine learning, clustering methods or generative AI (preferred but not required) Willingness and ability to collaborate with researchers from different backgrounds
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velocity changes at selected locations with the introduction of unsupervised machine learning and study the interaction of mass balance changes (crustal stress changes) and geohazards such as rain-induced
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willingness to learn A solid foundation in experimental research, data analysis, and scientific methods Interest in machine learning and data-driven approaches to materials discovery Strong interest in hands
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strong willingness to learn A solid foundation in experimental research, data analysis, and scientific methods Interest in machine learning and data-driven approaches to materials discovery Strong interest
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scienceEducation LevelMaster Degree or equivalent Skills/Qualifications Required Skills: Strong analytical background Proficiency in geometric deep learning and machine learning Prior experience in physics-informed
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with machine learning, clustering methods or generative AI (preferred but not required) Willingness and ability to collaborate with researchers from different backgrounds, stakeholders and the general
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100%, Zurich, fixed-term The Computational Mechanics of Building Materials in the Institute for Building Materials at ETH Zurich has an opening for a PhD student in modeling fracture in soft
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machine learning techniques. At the Department of Environmental Sciences within the research group Environmental Geosciences we are looking for a PhD candidate with interest in soil erosion research
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implementation of machine learning. PhD student position in the field of durability of wooden structures Your tasks Your tasks will focus on the design and operation of experiments to investigate the failure
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degree in Computer Science, Data Science, or a related technical field with strong foundations in machine learning OR Master's degree in Mathematics, Statistics, Physics, or other quantitative discipline