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performance simulation capabilities for gas turbine engines developed at Cranfield University as the starting point. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power
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or equivalent sanction that prevents, under the same terms, access to public employment in the relevant State. If an applicant, at any point during and before the completion of the recruitment process
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stages. Theoretical perspectives include organizational learning, resource-based view, micro-foundations, strategy process, with work spanning both qualitative and quantitative approaches. Our research
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. This position is part of a strategic research collaboration between DTU and the Korea Advanced Institute of Science and Technology (KAIST), combining expertise in biomedical signal processing, neuroengineering
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, please contact the supervisory team directly to find out more and discuss a project proposal before making an application to the lead university. You should indicate clearly on your application which
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that transduce environmental and developmental signals; recent relevant papers (2023-2024): https://doi.org/10.1093/plcell/koad288 https://doi.org/10.1371/journal.pbio.3002305 https://doi.org/10.15252/embj
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integrate metabolic signals, establish cellular memory, and regulate plasticity. We use cutting-edge methods (multi-omics, single cell, live cell imaging, modelling, chromatin biochemistry as well as various
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involves intricate signalling pathways, yet the priming factors, especially blood flow-induced mechanical cues, that initiate this process remain poorly understood. Recent advances in microfluidic organ-on-a
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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning