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Field
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to predict marine extremes for engineering and navigation purposes The PhD candidate is expected to develop an integrated framework that combines machine and deep learning methods with statistical and
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genetic risk scores in independent datasets. Experience evaluating risk scores or prediction models, including assessment of diagnostic or predictive performance in biomedical data. Desirable Criteria
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systems to operate safely and efficiently. Possible research directions include advanced flight control, model predictive control, nonlinear control, fault-tolerant control, cooperative control and aerial
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between pollution control efficiency, electrochemical yield, and energy recovery potential; • Develop coupled electrochemical and hydrodynamic models to predict process behavior; • Participate in
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functionalities that can further improve operational performance, such as the integration of predictive models, orbital dynamics knowledge, or drag-aware optimisation strategies to enhance manoeuvre timing and
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Intelligent Control Systems RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis
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, the mechanical state of the wall, or a defined combination of both. The resolved model will also predict the conditions under which the wall fails, with direct relevance to controlled, low-energy cell disruption
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(PAPROG) with the overarching goal to develop an operational modeling tool for glacier hazard prediction and analysis. This PhD will benefit from technical support funded by the project and collaborations
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-storage quality (firmness, colour, disorders) will be assessed. • Model development: Relationships between porosity, gas exchange, and storage outcomes will be modelled to evaluate predictive capacity
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The main objective of the project is to develop novel, interpretable predictive models for response to immunotherapy in patients with advanced melanoma, based on the functional activity of gut fungi