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Danfoss, you will combine thermofluid modelling, reduced-order multiphysics methods, and nonlinear rotor dynamics analysis to develop predictive modelling tools that support the industrial design of
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into these models. · Work with wet-lab biologists to design and implement appropriate experiments for collaborative work on model training, validation, and follow-up testing of predictions. · Explore
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and voltage-related tasks, to learn and predict voltages, branch flows/loadings, distribution factors and technical violations, and use the model as a fast surrogate within the hosting-capacity
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of the system, including laboratory testing and/or in situ monitoring campaigns. •Proposing predictive maintenance strategies based on the collected data and developed models, w ith the aim of optimising
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Maritime Ltd and the University of Southampton. Find out more about Knowledge Transfer Partnerships here: https://www.ktp-uk.org/ Compute Maritime Ltd is a London-based deep-tech company bringing
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, to develop solutions for predicting risk in international-scale financial markets. The project is developing event-triggered artificial intelligence approaches that complement existing financial risk models
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with modern analytics and predictive modeling, helping leadership and frontline fundraisers make informed decisions that advance philanthropy and engagement objectives. Key Responsibilities: Advancement
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geochemical alteration occurs, and the associated uncertainty in predicting reservoir behaviour. This will be done with laboratory investigations and geochemical modelling. Specifically, you will: Characterise
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models or prediction equations, stochastic simulation, site-response analysis, or probabilistic seismic hazard assessment Demonstrated experience in processing and quality-assuring strong-motion datasets
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modality-specific encoders, cross-modal fusion, acquisition-aware conditioning, and prediction heads. Develops and applies self-supervised training objectives (e.g., masked signal modeling, cross-modal