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Field
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technological advances that support the global transition toward net-zero emissions and sustainable aerospace engineering. Motivation The reliability of electric propulsion systems is pivotal for next-generation
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similar languages) Experience with large-scale neural network simulations Experience with analysing large-scale neural recordings Familiarity with neuroanatomy and neurophysiology Knowledge of dynamical
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-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique
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machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems
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economic studies funded by the UK National Institute for Health and Care Research. Experience of conducting economic evaluations using suitable statistical software (e.g. STATA, R or SAS) is essential
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, potentially including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during
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International applicants – January 2026 PhD: Applicants for PhD must hold an undergraduate degree at 2.1 level (or non-UK equivalent as defined by Swansea University) in Engineering or a similar relevant science
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Join our diverse and inclusive team to transform the future of aviation as part of the UK’s EPSRC Centre for Doctoral Training in Net Zero Aviation. Offering fully funded, multidisciplinary PhD
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faculty to develop their full potential. Diversity includes, but is not limited to, groups defined by race, nationality, ethnicity, age, gender, sexual orientation, language, religion, disability and/or
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software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics or related Experience in either machine learning or computational biology. Interest in both