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system model new module integration, scenario simulations, and prognostics analyses Physics-informed deep learning/hybrid modeling/reasoning AI algorithm development and optimization Job Requirements: A
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-CT analysis. Developing, calibrating and validating discrete element modelling (DEM) approaches to simulate degradation processes and slope behaviour. Translating laboratory and modelling outcomes
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through a model-driven approach, i.e. a combination of simulation- and data-driven methods and tools with data analysis and machine learning as an important part. The work builds on established theories and
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, including situations where some modalities are incomplete or unavailable. Exploring foundation-model and self-supervised learning approaches for extracting transferable representations from large-scale forest
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-and-motion studies, statistical analysis, and simulation modeling; collects and interprets operational data to support evidence-based decision making. • Oversees comprehensive annual facility
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developing simulation flows, analysis methodologies, and predictive models that enable Design-Technology and System-Technology Co-Optimization (DTCO/STCO) studies for future systems. You will work closely with
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programme of service development, Post-Award is evolving towards a more proactive, risk-based and resilient operating model. This role offers an exciting opportunity to contribute directly to that
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Nanopore). In addition, the role will include the collection and processing of DNA samples to create a training data set for age prediction machine learning models. Key responsibilities include: Undertake
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and experience in one or more open-source languages (e.g. Python, R). Prior experience/knowledge of computational psychiatry methods for modelling cognitive tasks. Experience in the use of neuroimaging
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learning for cardiovascular digital twins and AI-enabled precision treatment. The postholder will develop patient-specific models that integrate multimodal clinical, physiological, imaging and sensor data