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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions
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solutions; • Implementing and comparing different Intelligent Optimisation algorithms. • Implementing, providing and monitoring intelligent solutions (e.g., via API). • Producing documentation
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applying Natural Language Processing (NLP) algorithms. Knowledge or prior experience in Virtual Reality technologies. Work Plan: The grant aims to develop Agricultural Simulations using Virtual Reality as a
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requirement Work plan: The candidate will carry out R&D activities within the scope of the 2022.06672.PTDC project, namely: 1) Review of the literature on adaptive mesh generation algorithms for singular
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algorithms; - Automation of the model customization process by conducting laboratory tests.; - Improvement of the data workflow for real-time processing and sharing.; - Data collection in experimental and real
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learning algorithms, and design of optical communication networks or power consumption and energy saving. The synergies of MATCH consortium act together to enable the thirteen DCs to become the next
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testing. The work is expected to strengthen the reliability and predictive accuracy of SGB and related machine learning methods, i.e., those that share common algorithmic steps, in contaminated settings. 4
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algorithms for analyzing electrocardiography, electromyography and movement signals, identifying characteristics and recognizing patterns in everyday activities. Testing and validation of methods developed in
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algorithms to combine information on cardiovascular activity obtained from heart sound signals, electrocardiogram, and photoplethysmography. Investigate the inclusion of prior knowledge about the application
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algorithms for free-flying robots in microgravity.; • Implement and validate real-time multi-target tracking techniques.; • Validate the algorithms in simulated scenarios, evaluating robustness and accuracy