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Field
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the project/work tasks: The overall goal of this PhD project is to develop methodologies for real-time modeling and inversion of geophysical well logs, with a particular focus on borehole
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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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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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PhD Research Fellow in Chemistry-focus on Experiments in Chemical Kinetics and Heterogeneous Catalys
, catalytic reactors and/or gas-flow systems is highly beneficial Experience with and knowledge of mathematical modeling techniques (numerical solution of Partial Differential Equations) Language requirement
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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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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