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Machine Learning for Next Generation 5G Networks


School of Engineering and Computer Science

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Prof P Kourtessis , Dr A Papazafeiropoulos Applications accepted all year round Funded PhD Project (Students Worldwide)

About the Project

Applications are invited for one three-year, full-time PhD studentship available from April 2020, within the Cognitive Networks Lab of the Communications and Intelligent Systems Research Group at the University of Hertfordshire. For further information on our research visit

http://researchprofiles.herts.ac.uk/portal/en/persons/pandelis-kourtessis(44ce578c-ae79-410e-833e-c31e4fb2b7e2).html

http://researchprofiles.herts.ac.uk/portal/en/persons/anastasios-papazafeiropoulos(28e7ae56-3706-49d4-bb3d-a5b61f7701fb).html

Outline of the Projects:
We welcome the opportunity to support one PhD in research towards Machine Learning (ML) for next-generation networks (5G/6G). The Cognitive Networks Lab has a track record of successful research in the area of Communication Networks, Software Defined Networks, Massive MIMO Systems, Heterogeneous Networks, Caching, Machine Learning and Information Theory.

Machine learning (ML) is expected to become ubiquitous in next -generation networks, at all levels of design and operation, from digital coding, equalisation and impairment mitigation, through to monitoring, fault prediction and identification, and signal restoration, traffic pattern prediction and resource planning. ML tools must be tailored to the unique features of the wireless (potentially optical and satellite) environment and result in self-organising and self-optimising solutions. Such ML-driven solutions would be integrated across both the edge and core of the network providing centralized and distributed optimisation. The successful PhD candidate will therefore focus on providing a holistic approach towards the synergy and breakthrough implementation of ML to communication networks.

Informal enquires for both positions can be made to Dr Pandelis Kourtessis, Director of the Centre for Engineering Research by emailing [Email Address Removed].
Description of the University:

The University of Hertfordshire (UH) is based in Hatfield only a short (30 minute) train journey from central London. According to the UK-wide Research Excellence Framework 2014 over 85% of the submitted research at the UH was judged to be internationally excellent. The University of Hertfordshire is ranked among the Top 600 world universities according to the ‘Times Higher Education World University Rankings 2016’ and was awarded TEF Gold in 2018.

Applicant Requirements:
Applications are invited from individuals with a 1st or 2:1 in a relevant engineering or related scientific discipline. A Master’s degree or equivalent industrial experience would be desirable. We are seeking applicants with a good understanding of Machine Learning algorithms and/or Communication Networks and strong analytical and programming skills. Prior research experience in the application of Machine Learning algorithms to Communications Networks would be an advantage.

The studentship is available to both International and UK/EU applicants and provide a bursary of approximately £15,009 in 2019/20 and the two following years, together with payment of fees (£13,250 for international or £4975 for UK/EU applicants).

How to Apply:
Please download and complete an application form obtained from the following link:
http://www.herts.ac.uk/__data/assets/pdf_file/0010/31105/uh-application-form.pdf

Your completed application should be emailed to the email address below. You must complete section 11, titled personal statement/further information with a 500-word summary of your motivation to undertake your selected PhD project.

Please also send with your application a brief research proposal not exceeding 2 pages.

Doctoral College Admissions email: [Email Address Removed]

Closing date for applications: Position will remain open until filled.
Interview dates: TBC
Expected studentship start date: 1st April 2020 or as soon as possible thereafter.

Funding Notes

Annual tax-free bursary of approximately £15,009, plus tuition fees (£4975 for UK/EU or £13,250 for International applicants).


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