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their training and adaptation, Data modeling and quality assurance tools, Development of multi-level user interfaces, Classification and prediction models in environments with medical and health data, Explainable
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resolutions in 1,676 regions from 129 countries (see indicator 1.1.5 in the report and its appendix ). The chosen candidate will be involved in the following tasks: to use epidemiological models to model
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or biases in data collection, storage, and processing pipelines. Additionally, the candidate will develop AI models that can adapt to dynamic and evolving data environments, incorporating mechanisms
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project working to develop real-time vector-borne disease risk assessment in low resource areas. The individual will be directly responsible for the development of adaptive predictive models for nowcasting
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for a technician who will help with the quantitative analysis of a European travel survey and apply data science methodologies to predict travel behaviour in European cities as part of the EC funded
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Develop and validate material models and performance predictions using simulation tools Integrate materials into injection moulding and extrusion processes through prototyping Collaborate with
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of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models. Develop multistate sequence design algorithm for rational design of RNA switches. Develop database
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, as well as from industry. The successful candidate will work in the established collaboration between DSB and ICGI to develop multimodal deep learning models for predicting prostate cancer
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vegetation model. The new EEO-based vegetation model should then also be used to predict future transitions and biome shifts to ultimately answer the question to what extent C4 grasslands, savannahs and their
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so that we can improve the prediction, diagnosis, prevention and treatment of common diseases such as Alzheimer?s, cancer and cardiovascular disease. We take a computational approach focused on