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GTA-PhD Scholarship in in Adaptive Artificial Neural Networks

School of Computing and Digital Tech

Location:  Stoke campus
Tenure:   3-Year Fully Funded PhD Scholarship
Release Date:  Friday 05 June 2020
Closing Date:   Friday 31 July 2020
Reference:  CDT20-GTA4


Staffordshire University is pleased to announce a 3-year fully funded PhD scholarship in the School of Computing and Digital Technologies, to commence in September 2020 or January 2021. The successful candidates will receive an annual stipend of £15,285 with PhD tuition fees waived for three years. You will be expected to take up to 250 hours of teaching or teaching related activities per academic year in the School of Computing and Digital Technologies. This is also an excellent opportunity to develop your academic portfolio with HE teaching experience and benefit from the University’s training programmes. The posts are located on the Stoke-On-Trent campus.


UK, EU and international candidates are eligible for this scholarship.


Project title: Evolutionary Models for Adaptive Artificial Neural Networks

Supervisors:

Prof. Elhadj Benkhelifa, Professor of Computer Science and Artificial Intelligence, Staffordshire University (Principal Supervisor)

Dr. David White, Senior Lecturer, School of Computing and Digital Technologies, Staffordshire University

Dr. Maryam Shahpasand, Senior Lecturer, School of Computing and Digital Technologies, Staffordshire University


Applications are invited for a 3-year fully funded PhD scholarship in the area of Adaptive Neural Networks. 

Currently, Machine Learning solutions are critically dependent of their training. Neural Networks (NN) can only perform a specifically defined task and are unable to learn a process or a function that evolve in context. To solve these context changing problems, different techniques have been proposed but they still fall short in a number of aspects. Neuroevolution is one of the most promising methods, which constitutes a general and effective method that can be applied to a wide range of problems. It presents several advantages with respect to alternative training methods for neural networks. Therefore, more work needs to be done to improve adaptation of current neural networks models including Neuroevolution methods, so that they can adapt and evolve in context. During your PhD project, you will explore relevant solutions and develop and experiment with new adaptive models, which will be tested on relevant benchmarks and some real-world applications such as healthcare, games and finance.

Requirements

  • You will have or expect to have a UK Honours degree at 2.1 (or above), in Computer Science or related areas. A relevant Masters degree, or equivalent qualifications in Artificial Intelligence or related areas in Computer Science, is highly desirable

  • International applicants whose first language is not English are required to achieve IELTS 6.5 with a minimum score of 6 in each element

  • Good fundamental knowledge and experience in AI, Neural Networks and Deep Learning

  • Excellent programming Skills 

  • Excellent problem-solving skills

  • Good Writing and communication Skills

  • Able to teach in Programming


Informal inquiries can be made to Professor Elhadj Benkhelifa (e.benkhelifa@staffs.ac.uk ).


How to apply 

Please submit your application to Professor Elhadj Benkhelifa (e.benkhelifa@staffs.ac.uk ). by the deadline, with the following documents.

  1. Your CV

  2. Copies you’re your certificates – qualifications, with full transcripts

  3. A more detailed A PhD research proposal based on the project description above (Proposals on other projects will not be considered for the scholarship). Your proposal should be around 3000 words (tables, figures and references are not part of the word count). Your proposal should cover the following:

  4. A Critical Literature Review on the related work 

  5. Research Gaps

  6. How you intend to address the research gaps and potential contribution to knowledge

  7. Aims and Objectives and research plan for PhD completion

  8. List of 10 most relevant academic references

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