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The Child and adolescent Health Impacts of Learning Indoor environments under net zero (CHILI) Hub is a program funded by the MRC and NIHR, the goal of which is to understand the health effects we
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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary
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architectures and principles from Bayesian neural networks and biological sequence models, including large DNA and protein language models. The project also aims to develop a prototype federated learning
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The Child and adolescent Health Impacts of Learning Indoor environments under net zero (CHILI) Hub is a program funded by the MRC and NIHR, the goal of which is to understand the health effects we
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the SOFA framework to model the larva’s body dynamics. Create a mesh model of the larva with the main organs required for simulation and develop plugins to control muscle and body properties. Modelling
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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this, the impact of the heterogeneity in the structure of the individuals contact network on disease transmission will be investigated. The candidate will gain experience in a range of mathematical and computational
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. Integrate hydraulic-hydrologic modeling and surrogate models (e.g., Bayesian Networks) to simulate stormwater behavior under future scenarios. Apply optimization techniques to design and evaluate nature-based
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. Opportunities to participate in conferences, symposia, and networking events to share and enhance your research. Your role will be pivotal in driving AI innovation and contributing to a transformative approach to
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net zero goals and the future of our planet. During their lifetime, those energy storage systems can experience complex electrochemical-thermomechanical phenomena that can result in their volumetric