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). Development and validation of machine learning models for calculating occupational health indicators. Integration, management, and analysis of data from wearable monitoring devices. Experimental evaluation
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acquired across multiple anatomical regions to form a patient-level assessment. This PhD project will investigate novel deep learning methodologies that jointly model anatomical structure and prediction
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problems (e.g., optimisation, simulation, statistical analysis, or applied machine learning). ; - Participation in R&D projects with links to real-world or industrial contexts. ; - Relevant scientific
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profile: PhD in Agricultural Sciences;, Bachelor's and Master's degrees in Agronomic Engineering or related field;, Experience in applying machine learning techniques to agronomic data;, Experience in
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insurance, supported by INESC TEC. 2. OBJECTIVES: • Explore machine learning approaches for discovering interpretable and clinically relevant visual representations.; • Validate the proposed methodologies
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modelling, data analysis, and optimisation. Experience with machine learning or surrogate modelling techniques applied to offshore engineering problems. Experience in collaboration with the offshore wind and
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developing solutions for locating and manipulating semi-rigid objects or complex geometry. Investigating machine learning strategies with limited data, including the generation of synthetic data in simulation