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Field
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emissions, assess carbon oxides and NOx emissions for stable operation, and prepare a report; d) Collaborate with experimental groups to prepare a model of the existing lab-scale burner for numerical
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areas: Time-series analytics or forecasting Natural language processing (especially question answering or language grounding) Multimodal learning (e.g., combining text with temporal or numerical data
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in the development of novel sustainable catalysts, and support on reaction modelling to benchmark the microwave process against alternative technologies (as assessed by our partners). The successful
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directly can be incorporated into the model to improve simulation results. The work being conducted will therefore benefit mathematical fundamental research through modeling and numerical solution strategies
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the numerical model with experimental or existing numerical work Publish high-impact research Engage in interdisciplinary collaboration Job Requirements: Education qualifications PhD in Mechanical Engineering
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experience (or equivalent experience in engineering practice) and expertise at an appropriate level in geotechnical engineering Expertise in either geotechnical centrifuge modelling, field testing or numerical
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or equivalent experience in an appropriate discipline A background in numerical ice-sheet, glacier or fracture modelling, or equivalent experience Robust experience with computer coding (e.g., Python, C++) Track
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The lab of professor Jesper Tegnér at KAUST has openings for three postdoctoral fellowships in Data-driven Machine Learning for unbiased Discovery of Generative Models with special reference
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hazard? Do you want to further your career in one of the UK’s leading research intensive Universities? We are seeking a Research Fellow to develop new data-driven models of the magnetohydrodynamics
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and well data with state-of-art numerical forward modelling to understand the role of salt basin geometry and intra-salt heterogeneity on salt flow and the internal architecture of deformed salt bodies