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seeking a highly motivated doctoral student to study physics- and thermodynamics-informed machine learning (ML) for the mechanics of material failure. The project focuses on learning the effective behavior
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The Data-Driven Mechanics Laboratory is seeking a highly motivated doctoral student to study physics- and thermodynamics-informed machine learning (ML) for the mechanics of material failure
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mission is to accelerate the adoption of data science and machine learning techniques within the ETH Domain academic disciplines, the Swiss academic community at large, and the industrial sector. In
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mission is to accelerate the adoption of data science and machine learning techniques within the ETH Domain academic disciplines, the Swiss academic community at large, and the industrial sector. In
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relating to computer vision, machine learning and data infrastructure management. Part (50% effort) of this position would also support our work on DeepLabCut - a popular animal pose estimation system https
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of hardware sensors Your profile : You have a passion for real-time graphics, and an interest in learning and developing new skills in a small and dynamic team. You must have: Expertise in software development
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microfluidics and/or analytical chemistry in an asset Computer skills: Word, Excel, Powerpoint, some experience with at least one programming language language: fluent English self-motivation, scientific
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mission is to accelerate the adoption of data science and machine learning techniques within the ETH Domain academic disciplines, the Swiss academic community at large, and the industrial sector. In
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, minimum 2 years, in molecular biology, cell biology, physiology and pharmacology. Comfortable with programming concepts used in Data Science (R/Python) Good knowledge of common computer software (MS
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for machine-learning, particle physics and cosmology and is in close vicinity to the University of Geneva and CERN. The position comes with very competitive salary and benefits. Applicants should submit a