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, · quantifying uncertainty in causal links, · integrating the resulting models into neural networks (or other machine learning models) to detect and predict anomalies or anticipate failures. The research
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performance of diagnostic tests when these tests are imperfect. The case of plague in Madagascar in 2017. ten Bosch et al, PLoS Biology 2022 Development of an ensemble model to forecast COVID-19
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Postdoctoral position: Developing a human lymphoid organ-on-chip to evaluate candidate mRNA vaccines
Chakrabarti at the Pasteur Institute in Paris. The position is fully funded for 3 years in the frame of an industrial contract. Predicting the immunogenicity of candidate vaccines in humans remains a challenge
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) exhibit persistent dose-dependent m6A methylation levels and may serve as biomarkers of radiation exposure [12]. m6A methylation may also be used to predict radiation sensitivity [13] or to develop new
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health and translational medicine using a "bench-to-bedside" approach. By harmonising and analysing diverse biomedical data, while focusing on the secure data processing and predictive modelling, we aim
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properties and electrical characterization will be carried out. The results will be compared with ab initio calculations and will provide input for physical models based on real devices to predict key metrics
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. Measuring and predicting the effect of copy number variants on general intelligence in community-based samples. JAMA Psychiatry 2018 75(5):447-457. –Bourgeron T. From the genetic architecture to synaptic
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. Measuring and predicting the effect of copy number variants on general intelligence in community-based samples. JAMA Psychiatry 2018 75(5):447-457. Mercati O. et al. CNTN6 mutations are risk factors for