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1) Three fully funded PhD Studentships within the School of Computing, Creative Technologies and Engineering

Plus Icon Overview

The School of Computing, Creative Technologies and Engineering (SCCTE) is offering three full-time PhD studentships.  The awards cover the standard UK stipend (£14,553 in 2017/18; pro-rata into 12 monthly payments and exempt from UK Income Tax and National Insurance) and tuition fees (EU/UK fees only).

The PhD studentships include a range of exciting research projects and the appointed candidates will join the SCCTE. Academics within the School conduct research in a broad range of topics under four main groupings; Data Sciences, Cyber and Physical Security, Digital Health and Assistive Technologies and Information management.

The School comprises 70 academics and a growing number of researchers, currently standing at 45 PhD candidates.

We are seeking highly-motivated and enthusiastic students who will fully engage in the dynamic and vibrant research environment in the SCCTE.

Additional Information in relation to the More Life PhDs:

MoreLife, born from Leeds Beckett University, is located within the grounds of Leeds Beckett's Headingley campus. MoreLife deliver weight management and health improvement programmes to individuals, families, local communities and within workplaces. MoreLife was founded by Professor Paul Gately, one of the UK's most respected experts in obesity and nutrition, MoreLife’s heritage is anchored in its research and academic philosophy.

Plus Icon Projects

1. Machine Learning based activity classification and behavior understanding using wearable sensors (in collaboration with More Life)

An area of research within the School of Computing, Creative Technologies and Engineering at Leeds Beckett University is sensor data analytics, human activity recognition and behaviour understanding using wearable sensors.

In collaboration with the Carnegie School of Sports and More Life, this research will be applied to promoting health and well-being, and to support lifelong change in behaviour (e.g. to adopt health heating behaviours).

The goal of the project is to develop algorithms, systems and tools to monitor and assess change, and provide personalised feedback and recommendations based on measured activity, and other relevant parameters including physiological (measured non-invasively) as well as data aggregated from other sources.

The School wishes to further develop its existing strengths in artificial intelligence and machine learning by attracting outstanding doctoral candidates that are willing to work on state-of-the-art research projects, making an original and novel contribution to the field. The research will employ modern pattern recognition methods to identify patterns in sensor and other data and information fusion methods to combine information from multiple sensor modalities.

You should have:

  • A good first degree in computer science, software engineering, computer engineering or other numerate discipline e.g. electronic engineering with a significant computer programming content or MSc in computer science / artificial intelligence.
  • Good scientific programming skills (C++, C#, Python, ...).
  • Some familiarity with Artificial Intelligence specifically machine learning techniques, computational modelling techniques...

For a wider discussion before applying please contact Professor Dorothy Monekesso at D.N.Monekosso@leedsbeckett.ac.uk.

2. Machine Learning based activity classification and prediction of obesity related risk (in collaboration with More Life)

This project will investigate, select and apply appropriate machine learning and data science and mining techniques for the classification and prediction of risks or indicators associated with obesity.

Examples of relevant obesity related analysis include the prediction of weight gain, diet and physical activities based on recent trends and/or on combinations of biological, behavioural and environmental factors.

This research project will contribute to the ongoing collaboration between the School of Computing, Creative Technologies and Engineering and MoreLife, at Leeds Beckett University.

You should have:

  • A good first degree in computer science, software engineering, computer engineering or other numerate discipline e.g. electronic engineering with a significant computer programming content or MSc in computer science / artificial intelligence.
  • Good scientific programming skills (e.g. C++, Python and/or Matlab).
  • Some familiarity with Artificial Intelligence and Data Mining, especially machine learning techniques.
  • Positive attitude towards working as part of a collaborative research project.

For a wider discussion before applying please contact Professor Hissam Tawfik at H.Tawfik@leedsbeckett.ac.uk.

3. Computational intelligence-based optimisation of drones positions and movements for disaster management applications

This project will investigate, select and apply appropriate computational intelligence (particularly nature-inspired paradigms) and multi-criteria evaluation techniques for the optimisation of drones positioning and movement in disaster scenarios, within a simulated environment.

A key scientific challenge will be to achieve a high-level of optimisation performance based on multiple objectives, in such a dynamic environment.

This research project will build on an existing collaborative project (with University of Seville, Spain) which involves the use of artificial intelligence methods, namely genetic algorithms, for the efficient and dynamic adjustment of the locations of drones for natural disaster management, using simulation scenarios.

You should have:

  • A good first degree in computer science, software engineering, computer engineering or other numerate discipline e.g. electronic engineering with a significant computer programming content or MSc in computer science / artificial intelligence.
  • Good scientific programming skills (e.g. Python, Matlab or C++).
  • Familiarity with Artificial Intelligence especially evolutionary algorithms and/or machine learning.
  • Some familiarity with network simulation and/or ad hoc networks.
  • Positive attitude towards working as part of a collaborative research project.

For a wider discussion before applying please contact Professor Hissam Tawfik at Professor H.Tawfik@leedsbeckett.ac.uk

Plus Icon Application Process
  1. Applications should be submitted in writing to one of the research projects listed.
  2. Applicants should complete the research student application form and provide a research proposal using the criteria below as a guide.
  3. The research proposal can be up to four A4 pages in length (with references as an addition to the four-page proposal) using type Arial 12 point.
  4. Applicants should include the research project title.
  5. Applicants should use the following ‘Computing Studentships’ as the subject in the email subject line when submitting their applications.
  6. THE CLOSING DATE FOR APPLICATIONS IS MIDNIGHT ON 18th JUNE 2017

Read more information on the application process.

For all enquiries regarding the application process please contact:

Email:researchadmissions@leedsbeckett.ac.uk

Telephone: +44 (0)113 812 5375

Overseas Applicants - Prospective students from outside of the UK and EU who wish to apply to study at Leeds Beckett University will be required to make up the difference annually between the UK/EU fees to be paid by the University of £4,121 and overseas’ fees of £11,800. The fee difference must be paid prior to starting. Overseas applicants must refer to the UKBA regulations on studying in the UK and contact researchadmissions@leedsbeckett.ac.uk or telephone +44 (0)113 812 5385 before submitting.

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