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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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). Knowledge : PhD in ecological modelling, statistics, mathematics, geographical projections. Fluency in English (written, read and spoken). Transverse skills : Project management, Methodology and analytical
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; biogeochemistry and fisheries modelling). The PhD candidate will acquire and/or strengthen their understanding of interdisciplinary working, modelling and statistics applied to economics, trade-off analysis skills
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Targeted Project Complexflows, which aims to promote and structure the interactions of mathematics with other sciences around current issues. The thesis will be co-directed by F. Radjaï (LMGC, ED I2S 166
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organization. We invite applications from motivated candidates with passion and a strong interest in conducting research and innovation activities on crop modeling, remote sensing, and computer vision to predict
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of numerical ice shelf models has recently been developed to study, among other things, the realistic behaviour of the dynamics of a floe assembly, based on a granular approach. The purpose
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research institutions in Germany and a member of the Leibniz Association. The position is in the Statistical Modeling of Secondary and Registry Data Unit. The research interests of the unit are new
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
, applied mathematics, or a relevant engineering discipline Good programming skills and experience with numerical modeling Interest in performing experiments Excellent written and spoken English skills High
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this PhD project with biomarker data and outcome data, the consortium aims to develop a robust model that can better predict transplantation outcomes. This innovative approach will contribute to more
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. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set