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Field
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contribute to the development of next-generation modelling frameworks that combine physics-based hydrodynamic modelling with artificial intelligence (AI) and data-driven methods to better predict contaminant
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Experimental structural testing and instrumentation Numerical modelling and simulation AI/data-driven methods for engineering applications Familiarity with engineering software such as ABAQUS, ETABS, MATLAB
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(SingFarm)—a CREATE-NRF programme focused on building a sustainable, data-driven food production model with strong industry relevance to contribute Singapore’s ambition in achieving 20 per cent local
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processing, mining, chemical or geological engineering, or a PhD in a related field with mining/mineral processing experience. Knowledge of process mineralogy, froth flotation through past research, coursework
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pipelines for data processing, integration, and analysis, incorporating sequence analysis, structural modeling, and AI-based predictive tools. Collaborate with computational biologists and software engineers
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physics-based insights with data-driven methods—such as physics-informed neural networks, surrogate models and Bayesian optimisation—to explain formation behaviour, identify early indicators of cell
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modelling techniques, including data-driven methods and AI-assisted approaches, to evaluate system performance, explore system integration strategies, and assess techno-economic and environmental impacts
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25th April 2026 Languages English English English The Department of Structural Engineering has a vacancy for a Postdoctoral Fellow in Microstructure-Informed Modelling of Fracture in Cast Aluminium
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Engineering Prior hands-on experience with rat or mouse models (required) Additional Qualifications: At least 1–2 years of research experience in an academic or industry lab setting Experience with
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tomography, with numerical simulations informed by microstructural data. The successful candidate will work at the interface between experiments, modelling, and data-driven methods. Particular emphasis will be