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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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generation and predictive modeling by measuring the conductivity and permittivity of diverse electrolytes. The research will be structured into four key phases: (i) the design, fabrication, and validation
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modeling to create a predictive tool that spans orders of magnitude in length and time. Hands-On Numerical Modeling: Implement your model in a custom-made data analysis tool that uses advanced optimization
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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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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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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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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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Institut de Recherche en Génie Civil et Mécanique (GeM) | Saint Nazaire, Pays de la Loire | France | 17 days ago
or random fields or, propagated through the predictive models to improve their robustness. The measurements may also be compared with other mechanical characterisation techniques, including in situ approaches
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, combined with a predictive operational insights model to gain superior operational performance. Employed and supported by an academic team from the University, you will be based at ELE Advanced Technologies
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processing [1–3]. The experimental results obtained will be combined with a theoretical model enabling the prediction of equipment damage and service life, with the goal of optimising their operation and