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related field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy
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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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, antibiotic resistance genes, VOCs and PFAs. Investigations on electrode materials, manufacturing processes, signal amplification and modulation are underway. We strive to strengthen environmental applications
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analytical and problem-solving skills with high scientific curiosity. Ability to work independently and in a collaborative, interdisciplinary environment. Excellent communication skills in English (both
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with programming languages such as Python, MATLAB, or similar, and are interested in combining analytical modelling with data-driven or AI-based approaches. You are self-driven, curious, and able to work
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of fracture mechanics and/or SMA reinforcements Practical experience with FRP manufacturing and mechanical experiments Excellent communication skills and fluency in English (written and spoken); knowledge
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modelling and analysis is a strong asset. You are proficient working with programming languages such as Python, MATLAB, or similar, and are interested in combining analytical modelling with data-driven or AI
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is required Our offer The starting date is Autum 2026 or upon mutual agreement. The work will primarily be carried out at Empa in Dübendorf and should eventually lead to a PhD degree from EPFL. We live
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models and in vitro assays. Experience in phototherapy, programming and large data analysis is a plus. Excellent communication skills and fluency in English (written and oral). Our offer The project is