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Field
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, climate science, physics, mathematics, or a related field (PhD is advantageous) Experience with numerical modelling and analysis of large simulation datasets Knowledge of ocean biogeochemistry or Earth
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modelling skills to make a real-world clinical impact on early disease prediction and personalized medicine. Main duties and responsibilities The postholder will lead the numerical model development and
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developing simulation flows, analysis methodologies, and predictive models that enable Design-Technology and System-Technology Co-Optimization (DTCO/STCO) studies for future systems. You will work closely with
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at the Laplace laboratory, where numerical and analytical models are being developed to predict plasma potential control and flux entrainment from polarized electrodes. During the third year of the thesis project
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of the MOST (Modelling and Simulation of Turbulence) team focus on the numerical prediction of turbulent and multiphase flows with a broad range of objectives from fundamental understanding of flow properties
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assimilation on weather prediction Perform data-preprocessing, quality control, error modelling and correction Write reports and scientific publications Job Requirements: PhD qualification degree in Atmospheric
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methodological comparison between mechanistic vector control models and macroscopic SIR-type models calibrated on serological survey data. Currently, risk indicators (e.g., R₀ estimation) derived from mosquito
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, cardiac CT, ECG, portable ultrasound, and multimodal clinical data. Building scalable pipelines for image ingestion, DICOM/PACS interoperability, preprocessing, annotation, quality control, model training
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compatibility and drug product stability under clinical conditions. Apply machine learning, AI, and data science to predictive modeling, workflow acceleration, and decision-making. Author technical documents
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research programme in fundamental AI, AI co-scientists, and applications in complex scientific domains such as RNA sequence-function modelling, structure prediction, and inverse design. Further information