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, for the project “PREDICT-OAE project (From data to PREDICTion: modelling temporal ecological–biogeochemical links under Ocean Alkalinity Enhancement”, financed by internal funds from CIÊNCIAS and FCiências.ID
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event planning, execution and assessment. 12. Recruit, train, schedule and oversee student workers and interns. 13. Model a collaborative environment within the Office and across the University. 14
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captured from UAVs. The research will address the design of AI models capable of combining heterogeneous sensor modalities, including RGB, thermal, LiDAR, acoustic arrays, GPR, and X-ray backscatter
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of descriptive and predictive mathematical models. Examples of current and relevant problems in modern society that can be treated using such methodologies are ensuring the efficiency of industrial and
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al. 2019] and point-force Lagrangian models, with advanced post-processings [Vegad2024]. This work will be carried out with the YALES2 high-performance platform. Where to apply Website https
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
the discrepancy between theoretical predictions and the actual observed behavior. The objective is to develop model-based artificial neural network tools that combine the strengths of traditional numerical
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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs
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on building dynamic system models for both the energy conversion technologies and the greenhouse climate, integrating these into a unified framework suitable for state estimation, predictive control, and
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observational data, and the application of advanced methods for longitudinal and prediction modelling. You will also conduct methodological research on Bayesian methods and other innovative methodology
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Portuguese version A call is open for the award of one Research Fellowship within the scope of the project “PROSPER: Predictive models for sustainable protein recovery”, funded by FEDER and by National Funds