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our working and learning environment. We are seeking a highly motivated doctoral researcher to investigate how metabolic programs enable cancer cells to metastasize and colonize distant tissues using
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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures
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value diversity and respect in our working and learning environment. We are seeking a highly motivated doctoral researcher to investigate how metabolic programs enable cancer cells to metastasize and
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Opportunities to learn cutting edge techniques Perspectives for career development A diverse and interdisciplinary team Working, teaching and research at ETH Zurich We value diversity and sustainabilityIn line
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systems, and space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where
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, as well as retreats and study days provide numerous opportunities to exchange ideas with other doctoral students, receive feedback on the work in progress, and acquire the skills necessary
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and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure
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Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students
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of Zurich and Wageningen University & Research. The four-year STEPS project focusses on developing data-driven and machine learning methods to monitor CO2 and NOx emissions using the upcoming satellite
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opportunities and value diversity and respect in our working and learning environment. We are looking for a highly motivated doctoral researcher to develop new metabolomics strategy to advance and speed up anti