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(HTPB) and isocyanates for optimization of formulation (pot life) and product mechanical properties for application in solid rocket propellants. Due to the confidential and commercially sensitive nature
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• Strong quantitative and programming skills; experience with seismic data analysis or numerical modelling is highly desirable• Excellent written and verbal communication skills• Ability to work
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candidate will have a unique opportunity to work closely with researchers and technical experts from CSIRO in a multi-disciplinary team environment. The project will provide numerous research and development
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Description The NHR Graduate School for HPC Talents 9 Ph.D. scholarships for HPC, Numerical Simulations, and AI/ML projects The NHR Graduate School seeks qualified applicants interested in pursuing
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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, particularly GBS, continuous-variable QC Experience with numerical simulation, statistical estimation, or probabilistic modeling. Programming proficiency (Python, Matlab or C++), especially for numerical
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: This position is ideal for candidates with a background in civil engineering, structural engineering and mechanical engineering. Applicants should demonstrate: A strong interest in both experimental and numerical
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analyse the experimental data, developing techniques to extract information about the rotational orientation dependence of the gas-surface reaction, as well as performing numerical simulations to determine
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incorporating time-dependent source depletion. (4) Reducing uncertainty in groundwater risk assessments through refined numerical methods. (5) Applying the improved model to real-world groundwater contamination
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into neural networks. PINNs can model real-world signals with sparse, non-uniform, and noisy data. A key question is determining the optimal method for integrating physical priors into neural networks