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to predict thermal runaway on the cell level. The combustion and gas model developed on the cell level will then feed into the work to accurately predict thermal runaway on pack, module, and system levels
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to capture the spatial complexity of tumor organization and its relationship to treatment response. This PhD project aims to develop robust multimodal predictive models of platinum resistance using a large
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their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? We are looking for a recognised business development
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FLAME-GPU accelerated agent-based modelling of material response to environmental and operational loading EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce
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on advancing Predictive, Preventive, Personalized, and Participatory (P4) approaches in health and medicine. Within the IRAP framework, the project’s scientific goal is to discover and validate novel therapeutic
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, artificial intelligence and robotics, data science, cognitive and brain science and economics and finance. Leveraging its ‘4-in-1’ model of education and residential college system, UM provides all-round
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models. Misclassification Characterization (Month 4-5) Construct an augmented misclassification dataset containing: original and perturbed variants, model predictions, correct labels, perturbation type
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to work effectively in an interdisciplinary team. PREFERRED QUALIFICATIONS Experience with one or more of the following: knowledge graphs, graph machine learning, link prediction, representation learning
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/10.1007/s11142-024-09822-y . He, L.- Y., Wang, L. (2025). Can artificial intelligence curb greenwashing? Firm-level evidence based on large language model. Energy Economics, 152, 108954. https://doi.org
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of hybrid foundation model-graph neural network architectures for gene perturbation prediction, including the design and implementation of novel training strategies under experimental constraints, e.g