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Senior Research Associate in Computer Vision and Machine Learning
  • Machine Learning
  • Excel
  • Deep Learning
University of Bristol
Bristol BS8
125 days ago

Based in the Visual Information Laboratory within the Department of Computer Science at the University of Bristol, you will join a new and exciting collaboration between the University of Bristol and LV= General Insurance (LV=GI), taking a central role in a research project to develop novel computer vision and machine learning algorithms for use in in AI assisted decision making in the insurance domain. The post represents a unique opportunity to be part of an exciting agenda to understand and harness the power of recent advances in AI for the insurance and related sectors. The project will be led by Professor Andrew Calway and Dr Dima Damen.
The research will focus on fundamental problems relating to the processing of insurance claims. It will involve developing and evaluating novel computer vision and machine learning algorithms for reasoning about, and learning from, multi-modal data. Key applications include synthesising claim data, reconstruction of accidents and events, and predicting claim outcomes. You will be expected to take an active role in setting the research agenda within these areas in collaboration with the project leads and LV=GI.
You should have an excellent track record of research, at PhD and/or Post Doctorate level, in computer vision and/or machine learning, preferably with demonstrable experience of utilising deep learning techniques. We are especially looking for candidates who have a strong interest both in fundamental research and in engaging with an industrial partner to exploit research outcomes.
This post is offered on a full-time basis, with funding for 33 months, starting as soon as possible. It is anticipated that interviews will take place in late July 2020. Informal enquiries can be made to Professor Andrew Calway, Andrew.Calway@bristol.ac.uk.
We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and BAME communities, to join us.

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