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collaborate with the G2Elab modeling team on numerical models of quench dynamics. This development will aim, in the long term, to enhance our ability to predict quench behavior, as well as to serve as a tool
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Polytechnique de Toulouse (INPT). The PhD student will be part of a group of largely modelling PhD students all trying to make computational fluid dynamics modelling more sustainable in a comprehensive sense
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of the disease. A major constraint of most existing methods for cure models is the sufficient follow-up assumption, i.e. the study duration should be longer than the time span of possible events, which is rarely
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generative AI framework that utilizes machine learning predictions and quantum chemistry simulations to design stable, synthesizable, high-performance molecules. The framework will integrate multi-objective
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/ITS sequencing) to understand how residues drive nutrient cycling and soil health. Parameterise predictive models and translate outputs into practical grower tools, including a fertiliser credit
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recognition Individual and group behaviour understanding Long-term behaviour prediction Multi-task learning and foundation models Zero-shot and open-set recognition Privacy-preserving AI using anonymised human
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predict the behavior. You will be embedded in our team, which holds dedicated in-house expertise in 3D printing, pH-feedback systems and modeling, and you will have assistance on the various aspects
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psychometric data, with possible use of prediction modelling, mediation/moderation analyses, person-centred approaches, network analyses, or other advanced statistical methods depending on the final PhD focus
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across relevant temperature, composition, and redox conditions, and translating these insights into predictive models for industrial application. You will design and conduct high-temperature equilibration
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composition, temperature, and pressure, by utilising the experimental facility and modelling expertise at the Advanced Steel Research Centre (ASRC) of WMG. A mathematical model based on the experimental data