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planetary atmospheres. The Section also initiates and manages a wide range of related modelling, software and hardware R&D activities. You are encouraged to visit the ESA website: https://www.esa.int/ Field(s
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of multimodal prediction models to detect and predict atrial fibrillation (AF) and other clinically relevant cardiac rhythm patterns, predict disease progression and treatment response, and support personalised
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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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Position Data Science and Artificial Intelligence in Cardiac Electrophysiology Our goal: the development of multimodal prediction models to detect and predict atrial fibrillation (AF) and other clinically
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PhD students. The MoM section is recognized worldwide for its high-level research on experimental analysis, theoretical understanding and predictive modelling of complex thermo-mechanical behaviour (e.g
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these operations, and then train transformer models to predict sequences of operations in unseen data. Please see Weindel, Borst, & van Maanen, eLife, 2025, Weindel, van Maanen, & Borst, Imaging Neuroscience, 2024
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imperfect reflection of the influences of related variables. Structural causal models provide a flexible mathematical framework for efficient prediction, model selection and management in such settings
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screening and eligibility requirements, … Where to apply Website https://www.academictransfer.com/en/jobs/363367/phd-in-multiscale-modeling-of-s… Requirements Additional Information Website for additional job
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prostate cancer management. Research Objectives Predictive diagnosis: Should this patient undergo a prostate biopsy, and what is the likelihood that an identified lesion represents aggressive cancer
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a postdoctoral researcher, you will: Integrate hybrid traffic models and data assimilation methods into a coherent prediction framework. Develop uncertainty quantification methods and explainable and