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interesting collisions for further analysis. Within the NGT project, we seek to make best use of Machine Learning (ML) algorithms in this upgrade to exploit the full potential of the upgraded experiment
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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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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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multiphysics phenomena and complex multiscale processes, as well as in developing and using innovative scientific computing techniques (including HPC, machine learning, multiscale algorithms). It also has
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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
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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
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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