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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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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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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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essential, while experience with machine learning is advantageous but not strictly required. Excellent English skills, both in verbal and written communication, are required for the project. We are looking
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qualifications include a Master's degree in computational biology or a related field. Prior experience with programming, statistics and biomedical research is essential, while experience with machine learning is
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. Empa is a research institution of the ETH Domain. We are offering an exciting PhD position at Empa's Laboratory for Air Pollution / Environmental Technology, in collaboration with the University
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machine learning methods to monitor CO2 and NOx emissions using the upcoming satellite missions (e.g., CO2M, TANGO, Sentinel-4/5). Your research will contribute directly to monitoring global efforts
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equitable urban energy systems. Our work combines technology and policy with systems thinking and practical implementation, always grounded in real-world urban challenges. This PhD position is offered in