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thin films for emerging technologies. By combining state of the art physical vapor deposition, high throughput experimentation and data science, we develop thin films with enhanced performance and
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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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perform quantitative hydrogen analysis using electron spectroscopy. Main task of the PhD student is to establish the scientific basis for high-resolution electron recoil spectroscopy for surface hydrogen
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of measurement systems, signal processing and analysis and the assessment of measurement accuracy, robustness and long-term stability. The resulting data form the basis for model-based approaches to evaluating
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multifractal analysis, urban and energy planning, geography, and artificial intelligence to develop coherent and resilient approaches for urban energy infrastructures under land-use constraints such as No Net
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physics, PVD thin-film deposition, optical characterization methods, and advanced data analysis are advantageous Ability to work in a collaborative team environment towards a common goal Proficient English
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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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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
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. Empa is a research institution of the ETH Domain. To strengthen our team and enhance our knowledge and understanding in pyrolysis processes we are looking for a PhD student for scientific analysis
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talented candidate with a Master’s degree in civil engineering, mechanical engineering, or a related discipline Solid background in mechanics of materials and finite element analysis Sound knowledge