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in vitro and preclinical in vivo DMPK data into predictive, clinically relevant models that ensure therapeutic viability and streamline the regulatory pathway. 3) Utilize data-driven simulations
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and propulsion systems. You will be part of a multidisciplinary research team to develop advanced predictive maintenance solutions involving sensor integration, high-speed data acquisition, electrical
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of the MOST (Modelling and Simulation of Turbulence) team focus on the numerical prediction of turbulent and multiphase flows with a broad range of objectives from fundamental understanding of flow properties
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exposed to severe weather events, whose predictability is strongly limited by numerous factors: the complexity of the orography and land use, the presence of mountain— coast—sea transition zones, and the
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. The second project is “Forecasting Fertility with Foundational Models”. More specific tasks include: Conducting independent high-quality research on the limits of predicting fertility in relation to the PreFer
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brain functions and their alterations in experimental models of neurodegenerative diseases and mood disorders. the candidate must have solid experience in behavioral analyses commonly employed to study
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detailed knowledge of the performance parameters that affect their application software, as it aids in making future technology choices, predicting performance and scalability, and adapting critical software
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regularly in its various training and research activities, as well as the weekly seminars in IDAEA-CSIC. Where to apply Website https://www.idaea.csic.es/job-offer/phd-position-understanding-and-predicting-n
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) energy storage system for the simulation, analysis, and optimization of its operation within complex energy systems. The model will be used to support predictive energy resource management and the
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and innovation. (https://liu.se/loe ) You will work within the newly established Computational Materials Science group of Assistant Professor Waldemar Kaiser, part of the Theory & Modelling unit (led by