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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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for implementing response diversity enabled policy Network: ReDiLEEP - Using science-driven Response Diversity knowledge to Leverage Efficient Ecosystem Preservation and restoration Funding: Marie Skłodowska-Curie
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Overview This is an exciting opportunity for a Research Associate from an engineering (systems, control or signal processing) or computer science (machine learning or data-driven modelling
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validation. Job description Develop hybrid physical and data-driven models for condition assessment and lifetime prediction Develop and validate AI methods to identify degradation patterns in multisensor data
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future. They represent individuals driven by curiosity and a relentless pursuit of excellence. With us, they find the space to try things out and unfold their potential. Are you inspired by their passion
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-assurance framework and the Maritime Digital Twin. The role will also be involved in developing data-driven or physics-informed surrogate models of the FEA simulations to enable fast, generalizable damage
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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with organizational objectives (ASPIRE33, CARES, Magnet). Financial acumen with experience managing budgets, staffing models, and resource optimization. Change management, organizational development, and
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driven by elastic instabilities. Those kind of materials are usually called "active gels" and the theoretical analysis, that is the core of the research project, is based on continuum models of thin discs
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This PhD project will develop mathematical models to investigate population dynamics in biological systems. Combining dynamical systems theory, mathematical modelling, and data-driven approaches