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Field
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CFD technologies. As the PhD researcher on this project, you will investigate and develop the numerical and algorithmic components needed to make this hybrid high order to low order strategy practical
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algorithmic techniques needed to generate reliable high order meshes for complex, multiscale industrial geometries. You will work within a technically focused research group that maintains regular interaction
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Medicine Secondments: University of Ulm (D): To work with algorithms for wearable data University of Manchester (UK): To learn mathematical modelling of hormone rhythms. University of Bristol (UK): To learn
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training courses and workshops of the ENDOTRAIN network Research Fields: Hormones, Digital Health, Medical Sensors, Physiology Secondments: University of Ulm (Germany): Algorithms for wearable data analysis
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to seamlessly integrate complex hormonal data, high-resolution wearable sensor streams, and endocrine test outcomes. Intelligent Artifact Detection: Develop cutting-edge Machine Learning algorithms
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environment in Norway, and offer a wide range of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software
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interpreted by regression and tree-based machine learning algorithms to obtain even better mutants and develop mechanistic hypotheses. Various collaborations with ON-TRACT network partners across Europe allow a
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, you will develop the numerical, geometric and algorithmic techniques needed to generate reliable high order meshes for complex, multiscale industrial geometries. You will work within a technically
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reliable decision-making. Evolutionary algorithms will be employed to efficiently explore the parameter space and undertake sensitivity analyses. The integrated framework will be validated using analytical
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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and