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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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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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-based optimization, enabling autonomous measurement campaigns and real-time data assimilation. This research combines fluid mechanics, artificial intelligence, and robotics to establish the foundation
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of electron spectroscopy experiments Modelling of experimental data Your profile The position is immediately available for a candidate with a master's degree (or equivalent) preferably in physics, physical
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subtype of dementia. Your tasks This interdisciplinary PhD project combines atomic force microscopy, fluorescence microscopy, chemical spectroscopy, biophysics and data analytics. The PhD student will be
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) influence system performance and trade-offs. The research will combine analytical modelling with data-driven and AI-based methods, for example for scenario generation or uncertainty exploration. The PhD will
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development of electrochemical sensors detecting environmental pollutants, providing real-time information for effective management. Past and current work includes electrochemical sensors for airborne virus
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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