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join the Tang Lab. The Tang Lab (https://tangxinlab.org/ ) develops explainable, autonomous, and multimodal artificial intelligence (AI) systems to advance biological discovery. Our research integrates
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extent of domains. Radial Basis Functions (RBF) can be used to infer the boundaries between different domains in a probabilistic framework, thus accounting for uncertainty in the domain definitions
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be funded within the project titled ”A Probabilistic Inverse Model for Identifying the Source of Atmospheric Contamination on a Continental Scale” funding under the prestigious SONATA 20 competition
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Collaboration 'Probabilistic Paths to Quantum Field Theory,' which addresses theoretical questions at the interface between quantum field theory, probability theory, and noisy quantum dynamics. The successful
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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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implementing algorithms based on online Sparse Gaussian Processes and advanced probabilistic techniques enabling AUVs to dynamically alter their trajectories, cutting down on uncertainty and improving efficiency
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analysis / computer vision, ideally on microscopy or time-lapse data Experience in at least one of: tracking / time-series analysis, probabilistic modelling / uncertainty, real-time or streaming pipelines
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
excavation and ground response (e.g., geotechnical centrifuge testing, lab-scale TBM experiments). Probabilistic and reliability-based analysis applied to underground structures. Advanced subsurface
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. • Contribute to model adaptation by integrating climate, land use, and economic factors. • Develop vector risk projections and perform sensitivity analyses. • Produce and validate probabilistic forecasts
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/Computer Engineering, Computer Science, Applied Maths or related. Strong skills in AI techniques/ML/optimisation (Python/Matlab); familiarity with probabilistic modelling, time-series or control/power