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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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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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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
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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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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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candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments for all students, staff, and faculty. We invite individuals who will join us in our
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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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plans, in the treatment planning system matRad, in the robust evaluation of radiation therapy plans, modelling of radiobiological models applied to pancreatic cancer - information provided in the CV and
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the University of Manchester, and will deliver the objectives of an EPSRC research grant entitled “Input-restricted robust feedback control of nonlinear dynamical systems”. The recruited applicant will generalise
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software architecture. Drive research and development of algorithms for natural language understanding, structured command generation, and domain-specific model adaptation or fine-tuning. Integrate NLP and