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network performance data obtained from user devices. Assist in the development of basic models to predict or explain network behaviour under different conditions. Contribute to the improvement of internal
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postdoctoral researcher position in theoretical cosmology. The position is dedicated to developing a robust and efficient framework that incorporates a broad range of neutrino and dark-matter models, assessing
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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on these data, predictive models for early health warnings will be developed in collaboration with a statistician. The research is conducted in a multidisciplinary team across several institutions. The position
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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particle formation for atmospherically relevant molecules. ORCTOOL (Organic Cluster Tools) aims to create a toolbox for understanding and cost-effectively predicting the rate of massively multicomponent
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network performance data obtained from user devices. Assist in the development of basic models to predict or explain network behaviour under different conditions. Contribute to the improvement of internal
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financial goals through enrollment data analysis, predictive modeling, and decision support. This role combines deep analytical expertise with business acumen to transform complex data into actionable
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intelligence models for biomarker outcomes prediction and contribute to neural data analysis. Specific duties include, but are not limited to: Overseeing study MRI scanning procedure Coordinating study
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University Duties This PhD project is part of the AFLOW consortium supported by the Swedish Energy Agency and focuses on multi-scale modelling of aqueous organic redox flow batteries, to build a predictive