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sensors into mooring systems for real-time monitoring. • Develop numerical models to simulate the dynamic behavior of mooring and anchoring systems under different environmental conditions. • Analyze and
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analysis methodologies and numerical modelling tools for assessing structural behaviour, identifying damage, and predicting failures, using computational simulation and time series analysis techniques
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, supported by INESC TEC. 2. OBJECTIVES: - Broaden knowledge of the state of the art in the specific scientific field of the scholarship; - Identify and select appropriate methods for the study in
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autonomous platforms must operate for extended periods with limited external infrastructure.; ; This PhD scholarship will focus on developing cooperative autonomous navigation methods for teams of
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: Data processing nowadays unfold along the computing continuum, that is, merge the raw compute capacity of cloud providers, while being complemented by Edge processing units, that perform computation
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laser components, numerical simulation techniques, data analysis, and experimental research methodologies. 4. REQUIRED PROFILE: Admission requirements: Bachelor's degree in physics engineering or related
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benchmark for semi-supervised methods with foundational models in specialized medical imaging scenarios with limited data.; Development and validation of new semi-supervised techniques within this
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aggregation - Data analysis and implementation of forecasting models/algorithms in Python - Testing and validation of different forecast aggregation methods - Writing articles and technical documentation, and
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, techniques may include hyperspectral imaging, Raman spectroscopy, LIBS and/or XRF, together with calibration, multimodal co-registration, data fusion and machine-learning methods.; The research will
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learning, Graph Neural Networks (GNN)/Grid Foundation Models and/or probabilistic methods and Monte Carlo simulation; - Programming knowledge in Python; Minimum requirements: Previous academic background in