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Educational Master – and conducts research across a wide variety of domains in each of these fields. The faculty’s vision can be summarized as: “With trust, in connection, through continuous learning.” The FPPW
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manufacturing machines (looms, bobbinfeeders, ...) under dynamic conditions. Such simulations are very challenging due to the use of diverse materials (natural and synthetic fibers, yarns and fabrics) which
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in international research visits if needed. We are looking for a highly motivated researcher with: A PhD in machine learning, computer vision, remote sensing, glaciology, climate science, or a related
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, Electrical Engineering, Aerospace Engineering or a related field, with a focus on Robotic Perception and learning based methods Demonstrated expertise in at least one of the following areas: Machine Learning
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(ongoing PhD project). These pre-screened datasets will then be analyzed by various machine learning techniques (dimensionality reduction, unsupervised clustering, artificial neural networks, auto-encoders
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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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Actuarial Sciences (ISBA) of the UCLouvain is seeking a talented post-doctoral researcher to join us to develop methods for learning extremal dependence based on X-vines, with special attention for aspects
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to substantive political science questions. You have strong skills in automated text analysis and natural language processing (e.g., machine learning including neural networks, relation and entity extraction
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that ingest raw on-chain data (blocks, transactions, smart-contract events) from public blockchains into research-grade databases Developing statistical, graph, and/or machine learning models to study
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an answer,but also a machine-verifiable proof (or certificate) of correctness. However, a major limitation of current techniques is that correctness isnot proven relative to the human-understandable