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Field
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influenced corrosion (MIC) in marine environments. It uses AI-supported models, Bayesian data fusion, and real-time sensor data integration. Your responsibilities include: Development of a digital twin (DT
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key element of the two-beam acceleration concept Emphasize Bayesian optimization approaches and integrate these methods into the facility control system Design, execute, and analyze accelerator
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strategies to mitigate impacts on adjacent waters. Research activities include: coordinating with multiple stakeholders and collaborators to define objectives and research questions; leading participatory co
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, rental platforms, and production systems—where decision-making must balance conflicting objectives, leverage real-time data, and ultimately support sustainable profitability. Examples include optimizing
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In this role, you will be responsible of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
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uncertainty. Relevant areas include interpretable probabilistic, causal, Bayesian, and knowledge-based networks. While expertise in electromagnetics is not essential, applicants must be willing to broaden
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immediately. The ideal applicant will be highly qualified, motivated, and possess a strong academic background in robotics with a keen interest in advancing the frontiers of deformable object manipulation
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon