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, based on detailed studies of Earth and the solar system, is developing predictive models to identify habitable planets around other stars. Within three different research themes: (1) Planets and Early
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performance in hydrogenation, dehydrogenation, and hydrogen transfer reactions. Detailed characterization and kinetic studies will be performed to test computational predictions and microkinetic models, and to
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is to develop statistical methods for better understanding, description and prediction of patients’ conditions during cardiac arrest and cardiopulmonary resuscitation. This will require to develop and
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patterns and assess real-world effectiveness and safety outcomes, including survival, relapse, infection, and adverse events. Machine learning methods will be applied for risk prediction, signal detection
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hazards, enhancing asset protection, maritime security, emergency preparedness, and societal resilience. The project will leverage advanced AI and machine learning techniques to enable predictive risk
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About the position One or two position as a PhD Research Fellow in Mechanics: Viscous flow coupled with elastic deformations linking flow control and soft robotics is/are available at the Mechanics
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opportunity to develop a computational theoretical framework for predicting CIB fluctuations as a function of cosmological parameters. The candidate will work with time-ordered data from each of the above
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. Demonstrated experience applying machine learning and AI-based approaches to empirical disease, ecological, or biological datasets, with an emphasis on pattern identification, prediction, or spatial risk mapping
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. Machine learning methods will be applied for risk prediction, signal detection, and causal analyses, generating robust evidence to inform clinical practice and regulatory decision-making. A key objective is
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vehicles such as lipid nanoparticles (LNPs) • Create experimental protocols for cancerous, healthy human and microbial model cell membranes. • Establish predictive models for peptide-induced transport