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: $34,002 paDeadline: 31 August 2025Start date: after 1 January 2026Candidate Requirements• A first-class Honours or Master's degree in Physics, Geophysics, Computational Physics, or a related discipline
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to produce cutting-edge research. Prospective applicants must: Hold a good honours degree in an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine
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of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment
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SPINTEC fabrication facilities. As such, this thesis will be stationed mostly at SPINTEC in Grenoble, the PhD program being registered at Université Grenoble Alpes. You will also be part of the Vertical
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both the fundamental physics of electromagnetic materials and practical applications in 6G communications. The PhD is 4 years and funded by DSTL (Defence Science and Technology Laboratory), and you will
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 4 days ago
Physics invites applications from talented, ambitious graduating MSc students for a PhD position on Exploring Supernova Remnants in eROSITA and Multiwavelength Data (m/f/d) Job Code: 16/2025 The PhD project
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Computational verification of high-speed multi-material flows, where physical experimentation is highly limited, is seen as critical by the defence Sector (source: the UK Atomic Weapons
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Directed Energy Deposition (DED) process for metallic components. The PhD candidate will focus on edge computing and the application of AI for data analysis and for identifying correlations with ground truth
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | about 17 hours ago
Assistant position, with the successful candidate embarking on a PhD programme at LSHTM. It is anticipated that the role will lead to a further 18 month funded opportunity at Max Planck Institute
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust