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. This role focuses on developing and applying AI and deep learning techniques for analyzing high-dimensional omics data, identifying predictive biomarkers, and understanding cancer heterogeneity. Projects
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learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development of risk models and decision-support tools
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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
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population changes, and other demographic parameters (survival, fecundity, reproductive success, etc.). These integrated population models (IPMs) are increasingly used in ecology. They offer clear advantages
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-guided) Evolutionary trajectory analysis and fitness landscape modeling Integration of predictive algorithms with experimental iteration cycles High-throughput screening and selection platform development
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. Deep expertise in predictive modeling, classical ML algorithms (e.g., decision trees, gradient boosting), large language models (LLMs), generative AI, MLOps, and AutoML using frameworks like PyTorch
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, and clinical data. - Apply machine learning and foundational modeling to support predictive or exploratory analyses. - Collaborate with interdisciplinary teams to refine multi-modal pipelines
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related to staff position within a Research Infrastructure? No Offer Description Call for Postdoctoral Researchers in Artificial Intelligence (AI) – Focus on Large Language Models (LLMs) for Predictive
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in France is crucial to inform risk analysis and mathematical modelling, and obtain precious information for epidemic preparedness, particularly for outdoor farming systems which are highly prevalent
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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