138 machine-learning-"https:"-"https:"-"https:"-"https:" uni jobs at Cardiff University in United Kingdom
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- Learning Technologist The Cardiff Learning and Teaching Academy is offering an exciting opportunity to join its Digital Education Team. As part of a team of Digital Learning Professionals and Learning
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play a leading role in the analysis of large and complex genetic and electronic health records datasets with a range of Statistical and Machine Learning approaches, whilst leading a broad range of
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technology (post-editing machine translation and the University translation memory output) and software to ensure accuracy, consistency, and value for money in relation to translations. Instruct and guide
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play a leading role in the analysis of large and complex genetic and electronic health records datasets with a range of Bioinformatics tools, Statistical and Machine Learning approaches, whilst leading a
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experience within 2 of the following research fields: Programming in Python or Matlab EEG analysis (ERPs and Time Frequency) Machine learning Sleep scoring or analysis of sleep elements (like spindles or Sos
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machine learning techniques within research computing or data management environments 4. A degree or equivalent experience / professional qualifications 5. An understanding of ITIL service management
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, with various start dates. If you have difficulty accessing a computer, please call 029 2087 6137. Benefits We offer a leading and rewarding staff benefits package: 32 days holiday per year plus 8 bank
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stakeholders in the School and Student Futures to recommend clear processes enabling the delivery of successful placement and experiential learning activities that meet learning outcomes and professional
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computer and IT skills, Kinetics, Oracle, Microsoft Word, Excel, emails, including use of internet and online calendar. Level 3 Food Safety and Level 2 Health and Safety. Desirable CriteriaExperience
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advanced computer modelling (in silico), through robot driven testing of implanted knees (in vitro), to 3-dimensional X-ray imaging of moving patients (in vivo) with Machine Learning driven analysis