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Federated Learning for Intelligent Transportation Systems, Computer Science – PhD (Funded)


   College of Engineering, Mathematics and Physical Sciences

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  Dr Johan Wahlström, Dr Man Luo  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Location:

Streatham Campus, Exeter

General Information

The University of Exeter’s Department of Computer Science is inviting applications for a fully-funded PhD studentship to commence on 9 January 2023 or as soon as possible thereafter. For eligible students the studentship will cover Home tuition fees plus an annual tax-free stipend of at least £16,062 for 3.5 years full-time, or pro rata for part-time study. The student would be based in the Department of Computer Science in the Faculty of Environment, Science and Economy.

Supervisors

Dr Johan Wahlström, Computer Science, University of Exeter

Dr Man Luo, Computer Science, University of Exeter

Project Description:

Thanks to recent advances in microchip technology and AI algorithms, intelligence is moving from cloud-based architectures to decentralised entities, such as vehicles and smartphones. This transformation has given rise to the term federated learning, which refers to techniques for training machine learning algorithms in a collaborative and distributed manner. Federated learning is, among other things, expected to lead to significant developments in the transportation sector, developments which will be accelerated by the introduction of 5G networks, new developments in mobile edge computing, and an increasing number of sensing modalities. However, to fully realise the potential of federated learning in intelligent transportation systems, there are several obstacles that need to be overcome, including data privacy, scalability, and the lack of annotated data. 

This project will focus on how federated learning can be integrated into intelligent transportation systems, both as a way of improving upon existing transportation solutions, but also to develop novel applications enabled by the emergence of federated learning. The considered inference tasks may include high-level traffic analysis, demand prediction for vehicle sharing systems, transportation mode classification, and analysis of driving behaviour.

Funding

This award provides annual funding to cover Home tuition fees and a tax-free stipend. For students who pay Home tuition fees the award will cover the tuition fees in full, plus an annual tax-free stipend of at least £16,062. Students who pay international tuition fees are eligible to apply, but should note that the award will only provide payment for part of the international tuition fee and no stipend. 

International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.

The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic of Ireland) within the past 3 years or have applied for settled status under the EU Settlement Scheme.

Entry requirements

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology. Experience in programming, statistical modelling, machine learning, or sensors/IoT is desirable.

If English is not your first language you will need to meet the required level (Profile A) as per our guidance at https://www.exeter.ac.uk/pg-research/apply/english/

How to apply

In the application process you will be asked to upload several documents. 

• CV

• Letter of Application, including descriptions of a) why you would like to study for a PhD, b) why you

 would like to focus on this particular topic, c) any relevant expertise, d) your future career ambitions,

 and e) the qualities that you believe will make you a great researcher.

• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an

 interim transcript if you are still studying)

• Names of two referees familiar with your academic work. You are not required to obtain references

 yourself. We will request references directly from your referees if you are shortlisted.

• If you are not a national of a majority English-speaking country you will need to submit evidence of your

  proficiency in English.

The closing date for applications is midnight on 10 October 2022. Interviews will be held virtually / on the University of Exeter Streatham Campus in the week commencing 24 October 2022.

If you have any general enquiries about the application process please email [Email Address Removed] or phone 0300 555 60 60 (UK callers) +44 (0) 1392 723044 (EU/International callers).

Please ensure you quote Ref 4506 in any correspondence.

Project-specific queries should be directed to the project supervisors named above.


Funding Notes

This award provides annual funding to cover Home tuition fees and a tax-free stipend. For students who pay Home tuition fees the award will cover the tuition fees in full, plus an annual tax-free stipend of at least £16,062. Students who pay international tuition fees are eligible to apply, but should note that the award will only provide payment for part of the international tuition fee and no stipend.
International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK.
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