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methods at the intersection of scientific machine learning, reservoir/production engineering, and process systems engineering. This position will focus on the research of physics-informed AI-based models
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, flow, and process Optimization (AI-PRO) project. The goal of the project is to produce generalizable methods at the intersection of scientific machine learning, reservoir/production engineering, and
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-NorthWind webpage for more details): · Hybrid Multiscale Modelling · Hybrid Physics-Machine Learning Analyses · Coupled Atmosphere-Turbine Models · Uncertainty Quantification in Hybrid Frameworks We offer
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here . Personal characteristics To complete a doctoral degree (PhD), the candidate is expected to: demonstrate strong motivation, curiosity, and a learning-oriented mindset work independently, take
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embed user-defined conditions and structural constraints directly into machine learning routines to achieve high-resolution 3D interpolation of rock mass properties. These synthesized geotechnical fields
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Doctoral Programme Motivation to get proficient in Python Interest in sustainability science and strong incentive to learn Industrial Ecology methods, especially LCA analysis. Proven analytical and
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on innovative learning and teaching, and research, XJTLU draws on the strengths of its parent universities, and plays a pivotal role in facilitating access to China for UK and other institutional
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and strong incentive to learn Industrial Ecology methods like LCA analysis Proven analytical and computational capabilities, in courses and or/thesis deliverables Proven written and oral English
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and openness to learning new methodologies and techniques. Commitment to research integrity and ethical conduct in scientific investigations. Strong problem-solving abilities with a creative and
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-objective, real-time) and supply-chain optimization; PdM and RUL with health monitoring; digital twins/smart factories, cross-site transfer and federated/edge learning; uncertainty estimation and calibration