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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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to provide the supporting documents by the grant contracting stage. 3. Preferential Factors Proven experience with decision support systems based on knowledge bases and machine learning. 4. Work Plan The work
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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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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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vision based object detection tools, and reinforcement learning techniques. Additional Information Benefits Monthly Maintenance Allowance: €1,309.64 Funding Entity: Instituto Superior Técnico (IST
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, Economics, Management, or related fields. [1] ; Be a student enrolled in a doctoral program in Computer Engineering or Computer Science - a requirement to be duly proven at the time of hiring. 2; 3. Preferred
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | about 2 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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than 14/20 (1 point); B. Knowledge of Interactive Systems Design, Cyber-Physical Systems, Predictive Maintenance Systems, Automation, Machine Learning and Artificial Intelligence, Sensor Networks
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for applications for one research grant within the framework of project ISA4RL - Integrating Instance Space Analysis with Auto-Reinforcement Learning for Adaptive Algorithm Selection and Configuration
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machine-learning methods for sample segmentation and classification. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: The fellow will join the INESC TEC team within the LIBScan project, carrying