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stage. 3. Preferential Factors Proven experience with decision support systems based on knowledge bases and machine learning. Previous experience in machine learning applied to dynamic systems or orbital
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, and artificial intelligence, and with experience in probabilistic models,computer vision based object detection tools, and reinforcement learning techniques. Selection process Contest Evaluation Method
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 3 months ago
or related field # Numerical solution of partial differential equations # Scientific computing / high-performance computing # Algorithmic modeling and simulation # Fundamentals of machine learning or quantum
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EXPERIMENTAL, NUMERICAL AND MACHINE LEARNING”, funded by the “Programme, Innovation and Digital Transition (Compete 2030), European Regional Development Fund FEDER and national funds, Portugal 2030, Foundation
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or related field;* Solid knowledge in machine vision and deep learning (e.g., TensorFlow, PyTorch, OpenCV); Experience in Python programming (focus on libraries for data analysis and AI); Previous
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 3 months ago
creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search, interpretation
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Engineering or Industrial Engineering and Management) - 10 points; Others Masters – 2 points) b) Experience in applying machine learning algorithms, data preparation, normalization, feature selection, and
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be duly proven at the time of hiring. 2; 3. Preferred requirements: Experience using Machine Learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating
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for this grant: Requirement 1: - Be a student enrolled in a doctoral program in the area of Materials science, Machine Learning computational science, Coating and surface engineering a requirement to be duly
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application of statistical Machine Learning tools. WORK PLAN Collaborate in the following tasks of the project: a) Contribute to the design and development of the Life Cycle Assessment (LCA) system; b) Support