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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
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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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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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on novel applications of machine learning techniques on mobility data towards resilient, safe, inclusive and sustainable urban micro-mobility systems. You will become member of an international, 38-partner
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software for aerospace precision machining — you will develop physics-informed machine learning models that learn how individual machines actually behave, and use those models to drive a genuinely
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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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of the following: 3D Printing, Computer-Aided Design, Organic Modeling, Device Development, Software Development, Immersive Technologies (VR, AR), Device Manufacturing. An advanced degree may
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to the stage of contracting the scholarship, and before that, they may be replaced by a declaration of honor. Preferential factors: • Expertise in Machine Learning; • Experience in developing machine learning
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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and