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physics-integrated machine learning models—to predict, analyze, engineer, and understand microbial community dynamics. Applications span precision medicine and built environment microbiomes, with a strong
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to develop an aeromedical dispatch management software as a technology hub that provides data-driven prediction model and an automated dynamic decision model. The successful candidate will be responsible
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analyze particle precipitation from low-altitude spacecraft, in conjunction with particle and wave measurements from near-equatorial spacecraft, and theoretically model electron precipitation driven by
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SyMulDaM project involving the development of predictive models to quantify the integrity and durability of a nuclear power plant containment structure., within the mechanical engineering department
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questions for both Biogeography and Quaternary Palaeoecology, and the answers provide the basis for predictions of ecosystem and species response to future climatic change. We are looking for PhD candidates
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Computational modelling of two-dimensional graphene-based materials School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Natalia Martsinovich Application Deadline
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predictive control, optimization-based decision frameworks, and data-driven performance modelling. The overall goal is to develop computational methods that enable efficient and intelligent operation of wind
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inviting dynamic young scientists, capable of theoretical fracture mechanics and related modeling techniques, to join our team to probe cutting edge issues in fatigue and fracture. Some examples of research
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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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captured from UAVs. The research will address the design of AI models capable of combining heterogeneous sensor modalities, including RGB, thermal, LiDAR, acoustic arrays, GPR, and X-ray backscatter