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and assessing weld quality of mechanical parts in real-time by developing machine learning models that use sensor data and other tasks that are assigned to you. Core Responsibilities: Understanding
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to ETH's central compute and the Alps supercomputer at CSCS A multidisciplinary team of mechanical engineers, materials scientists, biologists, and machine learning researchers, embedded in the broader
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to Advanced Manufacturing, Solid Mechanics and Materials Design, Robotics, Control, Artificial Intelligence & Machine Learning for Mechanical Engineering Applications; Computer Science and Technology, including
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simulation, machine learning, battery manufacturing, electrochemical systems, materials characterization, or computational mechanics A strong publication record, preferably including at least three first
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, materials scientists, biologists, and machine learning researchers, embedded in the broader robotics ecosystem at ETH Zurich Strong ties to industry and to our spin-offs, and support for turning your research
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Post-Doctoral Associate in the Division of Engineering (Mechanical Engineering) - Dr. Mohammed Daqaq
and experimental, in the broad field of nonlinear mechanics. Preference will be given to applicants with expertise in machine learning, wave propagation, metamaterials, and/or fluid–structure
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analytics, including correlation analysis and machine learning techniques. Preferred Qualifications: Experience with microstructure characterization techniques (SEM, EBSD, TEM, XRD). Experience in mechanical
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100%, Zurich, fixed-term The Soft Robotics Lab within the Institute of Robotics and Intelligent Systems at ETH Zurich is inviting applications for a postdoctoral position within an industry-funded
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required to meet the eligibility criteria. Of secondary importance are: Experience with Python and relevant libraries for machine learning, optimization and simulation. Documented expertise in simulation
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potential to form a research program that incorporates recent advances in artificial intelligence, machine learning, and multidisciplinary design optimization for the synthesis of next-generation mechatronics