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the interrogation of chiral systems down to the nanometer scale. Project background Chirality describes whether an object is non-superimposable with its mirror image. Despite being a purely geometric property
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to the nanometer scale. Project background Chirality describes whether an object is non-superimposable with its mirror image. Despite being a purely geometric property, chirality plays a crucial role in all living
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recovery trajectories and injury patterns. Integrate personalized physiological measurements into a recovery prediction model, while adapting Bayesian Neural Networks for SCI data and analyzing the impact on
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aims to address this critical need by leveraging the power of spatial transcriptomics (ST) and high-plex immunofluorescence (IF) on a large cohort of archival CRC tissues. A key objective is to recapture
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-armed Bandits, Bayesian Optimization. Automated Model Design and Tuning: Neural Architecture Search, Hyperparameter Optimization. Computer Networking: Resource-Constrained Networking (e.g., Internet
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the admission requirements for a PhD at ETH Zurich Experience in machine learning, optimization, or AI-driven decision-making Preferably with knowledge of Bayesian optimization or Gaussian processes
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with the lab’s objectives. Support the next generation of scientists (PhD students, postdocs), fostering a collaborative and productive research environment. Key areas of support include design analysis
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multiphase fluid phenomena, such as bubble and droplet dynamics and the resulting fast flows. One of our key objectives is to control bubble oscillations to exploit their energy-focusing characteristics in
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to shape the direction of the project after the initial objectives are met. The project is funded through an ETH grant and will be supervised by Dr. Juliana Laszakovits in the group of Prof. Kristopher
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10%-30%, Zurich, fixed-term The Centre for Energy Policy and Economics (CEPE) at ETH Zurich is seeking a student research assistant to work on the project ‘Sustainable Consumption’. The objective