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Field
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address key challenges within these processes, constructing robust models and simulations that deepen the understanding of the underlying physics involved. The ultimate goal is to create predictive, physics
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to identify, quantify and compare these precursors using controlled laboratory experiments on granular systems combined with advanced optical measurements, with the objective of improving failure predictability
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, accessible, interoperable, reusable). Create high-quality, ML-ready datasets through feature extraction, multivariate analysis, and robust quality control workflows. Modelling & Machine Learning Develop hybrid
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profiling by time-of-flight mass spectrometry (ToF-SIMS and MALDI-ToF), the project will generate integrated molecular signatures that can be translated into robust monitoring assays of cancer cell
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project deliverables are met. Collaborate with the PI, Co-PIs and NTU team to design and develop a robust and scalable software system for autonomous agents navigating immersive 3D virtual environments (e.g
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across the following research areas: Predictive machine learning Robust and stochastic optimization Learning-enabled control and reinforcement learning Power system operations, planning, and electricity
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needs identified through workshops with robust scientific knowledge to design an accessible Virtual Reality (VR) communication strategy. This embodied and immersive experience will increase user
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analysis to enable robust assessment of data centre impacts on grid operations. The position offers the opportunity to work within a multidisciplinary research environment, engage with industry stakeholders
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foot-print air pollution sensors that can offer accuracy, robustness, and scalability. About the role The successful candidate will have the opportunity to develop expertise in integrated photonic and
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mechanisms of adaptive and acquired drug resistance, exploring network-level control and feedback in cell signaling systems, identifying novel drug targets and therapeutic strategies, and developing predictive