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, develop methods to speed up this development by focusing on machine learning techniques to address sustainability challenges in chemistry research. We will explore novel pathways to process design and
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machine learning and deep learning, where you must be creative and motivated in searching and reading relevant literature, and developing novel models to tackle complex real-world problems in healthcare
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100%, Zurich, fixed-term The Swiss Seismological Service (SED ) at the Department of Earth Sciences at ETH Zürich invites applications for a fully funded 4-year PhD position in Machine Learning
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candidates should have Solid working knowledge of software tools and environments for application deployment, optimization, and performance analysis Background in modern machine learning models, such as
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for a closed-loop decision support system that can be verified in rehabilitation in many health conditions. This position is open for a postdoctoral researcher in the field of transparent machine learning
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position in the area of Machine Learning for Engineering Design under the guidance of Prof. Mark Fuge, the Chair of Artificial Intelligence in Engineering Design. The general area of the laboratory covers
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mechanical experiments, control devices, acquire data and carry out post-processing operations. Other tasks comprise mechanical design (about 10%) and machining as well as electronics (about 10%). Examples
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novel methods at the intersection of advanced control, optimization, manufacturing science, and machine learning, to create the next generation of sustainable automation solutions for modern manufacturing
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at ETH Zurich. Our mission is to accelerate chemical discovery using digital tools. We predict chemical reactivity and molecular properties using the tools of machine learning, computational chemistry, and
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, or machine learning/data science applied to environmental problems. Project background Successful participants could use coupled global (CMIP) simulations, design and set up new model experiments using CESM2