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Field
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circuits and electronics integration. It is desirable that the candidates have sound knowledge in statistical analysis of device output and have experience with failure analysis. The role holder will work
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. Strong quantitative skills, with experience in econometrics and statistical analysis. Experience working with large datasets and appropriate software (e.g., Stata, R, Python, Julia, MATLAB). Excellent
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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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in a relevant discipline, such as medical statistics, and practical experience applying research skills to deliver high-quality outputs within specified timelines. A PhD, or ongoing PhD studies, in a
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analytical methods for the analysis of linked data from electronic health records and genomic or molecular sources. Strong statistical skills (e.g. proficiency in R or Stata), along with excellent writing
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with, cloud computing and virtualisation technologies Familiarity and hands-on experience with machine learning techniques desirable Desirable to have work experience (through internships or similar) in
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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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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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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