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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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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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25 Mar 2026 Job Information Organisation/Company Empa Research Field Chemistry » Other Engineering » Materials engineering Technology » Energy technology Researcher Profile First Stage Researcher
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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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Additional Information Website for additional job details https://academicpositions.com Work Location(s) Number of offers available1Company/InstituteEmpaCountrySwitzerlandCityDübendorfPostal
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with cell culture, 3D 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
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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