53 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" scholarships in Switzerland
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value diversity and sustainabilityIn line with our values , ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment
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equality of opportunity, value diversity 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
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-structure interactions of flapping flags (Bio-inspired) unsteady vortex formation and interaction More information about the lab and the ongoing and past projects can be found here: https://www.epfl.ch/labs
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predict protein-protein complementarity, design artificial protein binders, investigate the effects of mutations on protein structure and function, and apply protein representation learning to uncover
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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library. Strong interest in machine learning, reinforcement learning, and fluid dynamics. Ability to work independently and collaboratively in an interdisciplinary team. Excellent command of English, both
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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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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework
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topics. You collaborate on research and pedagogical projects and assist in academic administration to some degree. You will teach two hours per week (during the semester).