Information and Visualisation Design Methods (SAM01)

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Find out more: http://www.lboro.ac.uk/study/postgraduate/supporting-you/research/
Project detail
Big data and advanced data analysis are increasingly being used in engineering. Some examples include the use of machine learning models trained on labelled data to predict interesting features of product defects or process behaviour. Machine learning methods offer unprecedented opportunities to understand and support people, processes and ecosystems in various domains, however, they often produce difficult to understand analytical outputs. Some examples comprise predictions and recommendations based on complex transformations of input variables, multidimensional data representations of structured and unstructured data. In order to inform and improve decision making, these complex outputs need to be translated into effective communication insights.

The work will be undertaken within the Embedded Intelligent Integrated Systems Group at Wolfson School, as part of a multi-disciplinary team of engineers and scientists who are focused on opening up new areas of potential for intelligent systems.

The research topic will be specifically defined based on the background and interests of the applicant after acceptance. Applicants should have a strong background in design, mathematics and algorithms, excellent writing skills, and experience or genuine interest in smart systems.

Find out more: http://www.lboro.ac.uk/study/postgraduate/supporting-you/research/

Start date of studentship: 01 October 2020.
Entry requirements:
Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Computer Science, Engineering or a related subject. A relevant Master’s degree and/or experience in one or more of the following will be an advantage: robotics, computer science, applied math, electrical engineering.
How to apply:
All applications should be made online at http://www.lboro.ac.uk/study/apply/research/. Under programme name, select ‘Mechanical, Electrical & Manufacturing Engineering’.

Please quote reference number: SAM01