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Engineering - Research and Development in Lisbon Scholarship Theme: Spatiotemporal Models for Sustainable Mobility and Urban Health in Medium-Sized Cities Duration: 3 months Maximum Duration Including Renewals
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the project: Develop, train, and optimise deep learning models for wildlife species identification, classification, and segmentation using real-world datasets. Design and implement software modules to integrate
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reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability
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, machine-learning model development, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must
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machine learning (ML) approaches offer a powerful framework for modeling complex catalytic materials with near ab initio accuracy while enabling simulations at significantly larger spatial and temporal
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of: • machine learning • cybersecurity • distributed systems • privacy-enhancing technologies The research will be carried out within the (team name) at LS2N, focusing on trustworthy AI and cybersecurity
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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associate in the broad areas of high performance computing and machine learning. HighZ is focused on developing scalable high order methods, enhanced with surrogate models for subscale physics, for modeling
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research projects will be considered.) Technical expertise in machine learning and model fine-tuning – 10% Demonstrated experience with neural network training, loss function design, embedding-based models
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integrates spatio-temporal analyses (including synthetic descriptions such as distribution envelopes, size structures, and joint species distribution modeling), trophic modeling, and machine learning