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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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-leading, diverse and UK-wide research programme in probabilistic AI. The hub will develop the next generation of mathematically-rigorous, scalable and uncertainty-aware AI algorithms. This will be achieved
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methodologies for risk-based inspection and maintenance planning in wind turbine systems. You will work on designing a robust, data-driven decision framework that integrates structural models, probabilistic
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loadings. It combines experimental data, finite element simulations, and probabilistic models. Results depicted on Fig. 1 and Fig. 2 show high extrapolation and interpolation capabilities of the obtained
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of probabilistic prediction capabilities. Collaborate in the co-development of decision support tools that translate model outputs into actionable information for regional stakeholders, building on priorities
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Control / robotics / feedback systems Optical systems / optical tweezers / laser-based manipulation Probabilistic modelling / tracking Software in the loop (SiL) Strong mathematical / statistical skills and
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on microscopy or time-lapse data Experience in at least one of: tracking / time-series analysis, probabilistic modelling / uncertainty, real-time or streaming pipelines Strong mathematical / statistical
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), quantization and sharding, prompt optimization, reinforcement learning, Transformers/Deep-SSMs/Test-Time Regression. Experience with probabilistic machine learning, including but not limited to Gaussian
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work on designing a robust, data-driven decision framework that integrates structural models, probabilistic degradation processes, and operational monitoring data. Your research will focus on identifying
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explainable, embodiment-aware, and probabilistically consistent adaptation from onboard sensory data via end-to-end differentiable architectures, enhancing robustness, efficiency, and generalization across