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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
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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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Supervisors: Prof Ioan Notingher (School of Physics and Astronomy) Dr George Gordon and Dr Abdelkhalick Mohammad (Faculty of Engineering) Funding: fully-funded (stipend and PhD fees) Start date
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processing, embedded systems, machine learning, and networked communication. Each PhD position corresponds to a dedicated research topic within the consortium. All doctoral researchers will benefit from joint
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PhD Studentship in Aeronautics: Real-time machine learning and optimisation for extreme weather (AE0073) Start Date: Between 1 August 2026 and 1 July 2027 Introduction: Climate change is
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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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certification, dramatically accelerating innovation cycles. What you will gain: Expertise in Finite Element Analysis, Scientific Machine Learning, Uncertainty Quantification, and Professional Programming
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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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develop methodologies (such as acoustic emission method) detecting early signs of damage, leaks, or degradation before they become critical. We will also leverage the latest developments in machine learning
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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress