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Learning for Biomedical Data. The postholders will focus on developing and applying state-of-the-art generative models (such as VAEs, GANs, and transformer-based architectures) to large-scale biomedical
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Models for Multi-Modal Reasoning This project explores the integration of graph-based foundation models (e.g., knowledge graphs) with large language models (LLMs) to build AI systems capable of reasoning
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skills and curiosity about complex systems. Position Overview You will design and implement new computational and statistical models to reverse-engineer causal networks from noisy, high-dimensional, multi
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scales. The project involves the modelling of energy infrastructures, the development of scenario-based simulations, and the generation of actionable indicators to support decision-making. You will be part
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(TBI). The research will use in vitro and in vivo mouse models. Qualifications: 1). Recent PhD or equivalent degree in neuroscience, molecular biology, physiology, or cerebral vascular biology. 2
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change #utilize a participatory system dynamics modeling to match resilience patterns with best-fit learning cases from various regional contexts in Europe #develop a resilience performance framework for
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skills in data analysis, machine learning, as well as in mathematical and computational modelling? You will have the opportunity to investigate innovative solutions using machine learning algorithms and
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epidemiology to understand RNA metabolism. Perform stochastic simulations to analyze model behaviors. Fit the model parameters to empirical RNA expression and RNA-protein binding data. Predict outcomes
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trials (e.g., diet, FMT), and ex vivo gut models enabling advanced multi-omics analyses of these samples. In addition the lab also maintains a large culture collection, partially linked to genomic data
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fire weather index and burned area in ERA5 reanalysis over the Mediterranean, (2) these conditions will be tested in CMIP6 models, to (i) check whether the models can reproduce such conditions and (ii