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interest in social science applications, and with strong competence in statistics and machine learning. The successful candidate will develop predictive models using machine learning and work alongside other
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pathophysiology associated with inflammation will be used. The results obtained will then be integrated into the development of new in silico models for predicting the toxicity properties of the analyzed
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environment. Development of models to diagnose and predict battery performance and ageing. Participation in national and international research projects related with energy storage and its integration in
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recover quickly from disruptions. The research will involve reinforcement learning, predictive modeling, and real-time adaptive control to dynamically optimize production sequencing, resource allocation
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preparation. Be flexible and self-sufficient predicting tasks that need to be done. Auburn Spirit: Welcome guests to campus and demonstrate the Auburn spirit, fostering a positive, engaging environment
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Faculdade de Ciências Médicas|NOVA Medical School da Universidade NOVA de Lisboa. | Portugal | 1 day ago
supervision of the project’s Principal Investigators: Create a spectra library of arginine methylation peptides Train transformer models to predict MSMS spectra of arginine methylation peptides. Place of work
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development of new computational and mathematical models to quantify and predict infectious disease risk, particularly for identifying high risk individuals and groups. The PDRA will translate conceptual
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and trustworthy machine learning-based clinical prediction models. Funded by the Medical Research Council (MRC) and the National Institute for Health and Care Research (NIHR), the project aims
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creating a unified data framework for microbial carbon dioxide conversion and establishing a predictive AI modeling. Your profile The candidate is required to have a strong background in AI/machine learning
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, we aim to create autonomous “self-driving” microscopes that: build statistical models of biological dynamics in real time predict the most informative next experiment execute it automatically on living