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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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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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advanced data mining and machine learning techniques, we will extract valuable data from databases, data repositories and non-strucured data sources (e.g. scientific articles and supplementary materials
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100%, fixed-term We are seeking a postdoc in biogeochemical modelling to work on the SNF-funded N2O-SSA project: Combining measurements, modelling and machine learning to improve N2O accounting
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the machine learning domain to improve data analysis You will be responsible for project meetings, reporting, scientific publications, and conference/seminar presentations You will supervise master/bachelor
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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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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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Professor Dr. Rama Gottfried at the Institute of Computer Music and Sound Technology (ICST) at the Zurich University of the Arts (ZHdK). Project background The project aims to develop theoretical and
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scenarios and impacts, 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