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英国利兹贝克特大学计算和创新科技学院博士后
文章来源:知识人网整理       更新时间:2017年05月09日

School of Computing and Creative Technology Studentships

Leeds Beckett University

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

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.

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 eating 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...

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.

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.

 

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