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. The PhD programme A three year full-time study within the framework of the regular PhD programme (5+3 scheme), if you already have an education equivalent to a relevant Danish master’s degree
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outstanding students from all over the world to apply for PhD positions in our renowned PhD program. Based in Hamburg, our program offers excellent training and top-level structured supervision in climate
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need to be enrolled in one of the EPFL doctoral school programs. This is a separate application process that you can start in parallel. We suggest that you apply to the Doctoral Program in Mechanics
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Two fully-funded 3-year PhD studentships are available in Neuromorphic and Bio-inspired computing at the interface between control engineering, electrical engineering, computational neuroscience
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, process stability, and the downstream consolidation and performance of remanufactured composites. This fully-funded PhD project fits within a wider research programme with industrial partners and an
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dedicated PhD student with a 1st class or 2:1 degree in the field of Engineering, Mathematics, Physics, Architecture or Computer Science. A MSc degree in a relevant area is desirable though not necessary
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relevant monitoring activities organised each year.Candidates can review the admission process on the website: https://icn2.cat/en/careers/phd-programme/who-when-howHow to apply:All applications must be done
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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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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | about 6 hours 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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, 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