64 web-programmer-developer-"https:" "https:" "https:" "https:" "https:" "https:" PhD scholarships at Newcastle University in United Kingdom
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the Stability of Vortices in Planetary Atmospheres and Oceans (Studentship code MSP113; Programme code 8080F) The rapid growth of supermassive black holes at high redshift (Studentship code MSP114; Programme code
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integration. About This PhD This PhD programme is part of a prestigious £8M EPSRC Programme Grant on Advanced Integrated Motor Drives (AIMD), a major research initiative launched in 2025. You will work on the
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Scottish Power as part of your program. The project Power Electronic Devices (PEDs) have been proposed as a new approach to increase the uptake of low-carbon technologies (such as electric vehicles and heat
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, thermal and fluid-structure interaction performance of dynamic flexible cables and develop novel engineering solutions to enhance their operational reliability. The PhD student will combine mathematical
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. To effectively plan for water supply resilience, it is essential to robustly model future changes in hydrological systems. This project will develop a robust modelling framework to simulate future changes in water
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PhD Studentship: Development of a robust hydrological modelling framework for drought risk assessments Award Summary This studentship provides a tax-free annual living allowance of £25,726 plus a
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assemblage changes. Determining how coral reef fish food webs and energy pathways are connected along extended depth gradients of 0-90 meters. Based in Newcastle, and with international supervision, you will
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communities at different positions along depth, latitude, and longitude gradients; challenging these networks under hypothesized future assemblage changes. Determining how coral reef fish food webs and energy
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will also be provided. Overview This project will develop an adaptable Machine Learning (ML) hardware architecture to solve Artificial Intelligence (AI) classification tasks using Internet of Things (IoT
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-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms, using signal processing/machine learning techniques, to realise all-weather perception in