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statistical physics, applied probability, and population genetics; develop inference frameworks that link model predictions to genomic and epidemiological data; design controlled computational experiments
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, resilience and evolution of marine life to develop solid theories and predictive models of the relationships between marine biodiversity and ecosystem functions, which will in turn lead to improved economic
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the definition of requirements and architecture to the development of predictive logic, local control systems, automotive-based and second-life storage systems, up to the VPP (Virtual Power Plant) aggregation
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among multiple turbines in order to optimize their performance through the implementation of appropriate control techniques. As part of the research activity, medium- and low-fidelity codes will be
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across Predictive AI and Generative AI use cases. Education & Experience Master's degree (required) or Ph.D. (optional) in Computer Science, Engineering, Applied Mathematics, Statistics, or a closely
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Materials, Bioinspired Materials and Sustainable Materials. For more details, please view https://www.ntu.edu.sg/mse/research . We are looking for a Research Fellow on the design, synthesis and applications
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different rescripting strategies reduce the impact of emotional memories, and whether these changes in memory predict subsequent symptom reduction in a student population. This work will be followed by
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advanced predictive control methods to optimize the operation of an integrated system of hybrid energy storage systems (i.e., multi-carrier energy hub or micro-grid) and high-tech greenhouses. These hubs
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of pharmaceutical formulation and manufacturing processes. The role The post holder will develop and implement mechanistic models to analyse and predict the behaviour of pharmaceutical processes. Your work will
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predictive control, optimization-based decision frameworks, and data-driven performance modelling. The overall goal is to develop computational methods that enable efficient and intelligent operation of wind