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Technical Proficiency: Familiarity with engineering tools with focus on design automation Experience in programming (e.g. Python) or machine learning and a desire to deepen your expertise Experience in
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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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100%, Zurich, fixed-term Prof. Dr. Kent Warren , head of the Solar Energy Engineering Laboratory in the Department of Mechanical and Process Engineering at ETH Zürich, invites applications for a
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100%, Zurich, fixed-term Prof. Dr. Kent Warren , head of the Solar Energy Engineering Laboratory in the Department of Mechanical and Process Engineering at ETH Zürich, invites applications for a
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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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metals), robotic fabrication, circular engineering, experimental methods, and topology optimization; Fluent in English (oral and written); Experience with computer aided design (CAD, e.g., AutoCad, Rhino
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business applications. Profile Required Qualifications Master's degree in Computer Science, Data Science, or a related technical field with strong foundations in machine learning OR Master's degree in
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these projects will bring forward the integration of novel methods at the intersection of advanced control, optimization, manufacturing science, robotics, and machine learning. The two doctoral student positions
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of fundamental knowledge about fracture in soft and slender bodies. Soft materials such as hydrogels and elastomers are increasingly used in engineering applications, from biomedical devices to soft robotics
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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