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design strategies, while producing structured spatio-temporal datasets that will serve as input for realising predictive models. Objective 3 — Realize predictive tools for scenario-based assessment
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Helmholtz-Zentrum für Infektionsforschung GmbH | Braunschweig, Niedersachsen | Germany | 24 days ago
Description The department EPID, led by Prof. Berit Lange, at the Helmholtz Centre for Infection Research (HZI), isoffering the position of a Doctoral researcher (f/m/d) Modelling vector-borne diseases in
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prediction. The project offers a unique interdisciplinary environment combining engineering, quantitative modelling, clinical medicine, and translational diagnostics. You will collaborate closely with
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Project This project aims to develop the Amazonian Early Warning System (AmEWS) - a near real time, data- and model-driven platform that focuses on multi-hazard risk prediction for the Amazon rainforest
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characterised and used to train and validate ML models that link synthesis conditions to performance metrics, enabling predictive optimisation of aerogel-based supercapacitors. The project will be undertaken
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. Research will have a strong focus on computational analysis or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or
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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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application! Your work assignments Spatio-temporal processes are everywhere in science and engineering, with applications ranging from weather prediction to cardiovascular medicine. Developing machine learning
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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state-of-the-art tools and AI libraries developed at the TU/e, such as GameBus and Experiencer. The collected data forms the basis for developing predictive AI models that tailor coaching content and