8 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Babes-Bolyai-University" positions at Empa in Switzerland
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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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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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worked in or with the defence sector Has programming experience in Python and Pyomo Due to the position’s close collaboration with the Swiss defence industry and the armed forces, preference will be given
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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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thermoplastic feedstocks for FDM printing Programming of an existing hybrid FDM printing machine, including printing and milling devices Modelling and optimization of the debinding process Simulation-based design
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offer a two-year post-doc position at Empa to conduct cutting-edge research in sustainable polymer synthesis and the possibility to acquire additional funding. You get access to top-analytical laboatories