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Field
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. The successful candidate will develop advanced skills in multi-modal sensor fusion, signal processing, machine learning, and integrity assessment, as well as transferable abilities in critical thinking, project
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, providing an open, welcoming and inclusive environment that promotes equality, values diversity and maintains a working, learning and social environment in which the rights and dignity of all our staff and
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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that by combining electrospray deposition (ESD) with scanning tunnelling microscopy (STM) it is possible to deposit intact conjugated polymers in ultrahigh vacuum (UHV) and to acquire unprecedented sub
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" (Supervisor: Prof Timothy O'Leary) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic
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models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based
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factors (learning curve). These will be linked with the CCTA imaging biomarker data (quantitative plaque analysis) that is being collected as part of the EU HORIZON TARGET study to determine personalised
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association of candidate genes with these surfaces. The project offers a unique opportunity to acquire a broad skill set of biochemical, biophysical, bioimaging and molecular techniques while working with a
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unique opportunity to acquire a very broad skill set of biochemical, biophysical, bioimaging and molecular techniques, combined with diverse data analysis and bioinformatics skills. The project would suit
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Entry Requirements - Swansea University Knowledge in biology is not essential, but the candidate will be required to learn appropriate biological and clinical knowledge in order to develop mathematical