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with experimentalists to validate predictions made by their machine-learning models and drive wet-lab discoveries. The candidate may also have opportunities to work with research software engineers
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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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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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required. The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative- genomics/) at Institut Pasteur, led by Laura Cantini, works at
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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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. Familiarity with frameworks such as TensorFlow and Keras, as well as libraries including Scikit-learn, NumPy, and pandas; - Experience with machine learning models such as Extreme Learning Machine (ELM
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skills in remote sensing, geospatial data analysis, artificial intelligence or machine learning, and environmental or agro-meteorological modelling, as well as experience handling large Earth observation
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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