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Willingness and ability to work in an interdisciplinary research environment at the intersection of history, data science, and digital humanities Familiarity with AI methods, large language models, or knowledge
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learning analysis of biomedical data and bioscientific programming for projects on neurological diseases. The candidate should have experience in the analysis of large-scale biomedical data (omics, clinical
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areas Prior experience in large-scale data processing and statistics / machine learning is required Previous work and publications in bioinformatics analysis of large-scale biomedical data, e.g., omics
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perturbation prediction, including the design and implementation of novel training strategies under experimental constraints, e.g., active learning and other data-efficient approachesConduct large-scale
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Cardioembolic Stroke Risk Stratification using AI Accelerated Patient-Specific Blood Flow Simulation
stratification. Then, AI-based surrogate models will be developed in order to obtain fast simulations for large-scale risk stratification. The CFD model of the LA and the AI-based surrogate models will be
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supercomputer for large-scale simulation and adversarial testing The project also includes industrial validation with social robotics platforms (e.g., QTrobot) for deployment in educational and special-needs
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or Chemistry. Mass spectrometry-based proteomics. Data analysis of large proteomics datasets. Experience in cell culture and molecular biology. Languages skills: fluent command of English; knowledge of another
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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Call for expression of interest SENIOR OR MID-CAREER RESEARCHERS IN ARTIFICIAL INTELLIGENCE, DATA SCIENCE AND COMPUTATIONAL SCIENCES AT THE PARIS BRAIN INSTITUTE PARIS, FRANCE The Paris Brain
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, spatial relationships, and contextual information. The framework would focus on real-time inference and investigate temporal search strategies using vision–language large models to identify anomalies in