To take a lead role in designing machine/deep learning strategies for data-toknowledge conversion, based on the characteristics of the available data and heuristic understanding. To develop the prototype algorithms and prove their functionality using a number of data sets, including applications to biotechnology and biomedicine. To demonstrate excellent research skills and ability to work both independently and as part of a team. To assist in planning a development plan beyond the scope of the proof of concept, to be included in future funding bids. To publish research outputs in high impact journals
Previous research and implementation experience in machine learning, modelling and data visualisation. Engage in high level research. Analysis of complex research ideas. Can apply existing methodology according to the overall context. Previously worked on relevant research projects Ability to convey complex information or ideas of detailed technical or specialist nature. Plan, prioritise and organise their own research/resources to achieve agreed objectives. Regular travel will be required associated with the collaboration between Teesside, York and Hull. Be self-directed and supportive and encouraging of others in a team. Liaise with people and participate in networks internally and externally, as required, to ensure good dissemination of research activities and facilitate collaborative work
Job details Job title Research Fellow (Machine Learning & Bioinformatics) Job reference D3774 Application closing date 18/12/2018 Location School of Science, Engineering and Design Salary £37,345 - £50,132 Status Full Time, Temporary until 31 March 2021 Job advert The post is part of the THYME project funded by Research England's Connecting Capability Fund. You will a take a lead role in designing the data-to-knowledge strategy with applications to bioinformatics. The post will be based between Teesside University Middlesbrough and Darlington Campuses, exploiting the opportunities offered by the new GBP 22M National Horizons Centre, a significant investment in multi-omics and process analytical technology. The Research Associate will develop and adapt machine/deep learning techniques to extract knowledge from available data , with applications to biotechnological and biomedical case studies. The successful candidate will investigate the effective use of the broad data sources arising, and extract knowledge using data analytics, modelling, machine/deep learning techniques. Given the 'proof of concept' nature of the post, fault finding and problem solving will be a critical skill. Some background in biology is desirable but not a requirement The successful candidate must have previous research and implementation experience in machine learning, modelling and data visualisation and be able to engage in high level research and analysis of complex research ideas. A PhD degree in computer science or related subjects, e.g. bioinformatics, mathematics, physics, or engineering, with related experience and with the relevant technical, professional or specialist knowledge to carry out the role is desirable, but not essential, for this role. Regular travel will be required associated with the collaboration between Teesside, York and Hull. Job description Person specification Additional information
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