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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078) Start: Between 1 August 2026 and 1 July 2027
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learning and machine learning for biological data Sequence and structure analysis of large-scale datasets Functional annotation and evolutionary analysis Collaborative research with experimental virology
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning, quantum computing
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intensity of these changes. This PhD project will ultimately enable aircraft to reroute safely and efficiently in real time as weather evolves. By merging scientific machine learning, large-scale data
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fully funded PhD position within the LowDataML doctoral network, focusing on developing innovative machine-learning approaches for drug discovery under low-data conditions. LowDataML aims to bridge
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering
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, electrical engineering, and computer science. Desirable skills/interests: Signal processing, machine learning, applied optimisation. Objectives: To analyse large-scale data that is reliable and collected from
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their pandemic potential and classification as bioweapons. This project aims to develop a machine learning-accelerated NMR platform for the discovery of high-affinity inhibitors targeting viral RNAPs. Building
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Job Description The section for Atomic Scale Materials Modelling (ASM) at DTU Energy is looking for two outstanding candidates for PhD scholarships within the field of Geometric deep learning