10 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Université-Laval-" PhD positions at Empa in Switzerland
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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 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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. Please submit these exclusively via our job portal. Applications by e-mail and by post will not be considered. Where to apply Website https://academicpositions.com/ad/empa/2025/phd-position-in-data-driven
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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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-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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and coordinate generation of new datasets for the scientific problems of interest. Analyse the data, write scientific manuscripts and present results at international conferences. Your profile Required
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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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, based on measured air temperature and humidity data in cold chains by commercial sensors, and deploy them in end-to-end virtual supply chains. This project also aims to optimize other thermal processes
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types of textiles Data management, data treatment Reporting and publication Your profile If you are interested in making a major impact on functional clothing and textiles, are highly motivated and
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components and nucleic acids interact and self-assemble. Apply data analysis and modeling to deepen understanding of nanoparticle architectures, and contribute to standardization-relevant method development