Machine Learning for Telecommunication Networks PhD

Mechanical, Electrical and Manufacturing Engineering
Entry requirements:
3 years
not available
Reference number:
Start date:
01 October 2018
Is funding available?
UK/EU fees:
International fees:
Application deadline:
09 March 2018



in the UK for research quality

REF 2014


in the UK for Mechanical Engineering

The Complete University Guide 2018


of 2 Queen's Anniversary Prizes


Loughborough University is a top-ten rated university in England for research intensity (REF2014) and an outstanding 66% of the work of Loughborough’s academic staff who were eligible to be submitted to the REF was judged as ‘world-leading’ or ‘internationally excellent’, compared to a national average figure of 43%.

In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career. 

Project detail

We invite applications for a 3-year PhD studentship to study machine learning algorithms aiming to enable emerging applications for telecommunications networks, in the Wolfson School of Mechanical, Electrical and Manufacturing Engineering at Loughborough University. The successful applicant will join the Signal Processing and Networks Research Group, under the supervision of Dr. Mahsa Derakhshani.

This project aims to perform fundamental research in telecommunications networks, focusing on developing algorithms to control and analyse smart and large-scale networks such as smart cities, vehicular networks, and Internet-of-Things (IoT). Learning-based algorithms can enable the system to understand the network dynamism, forecast based on the context information, and proactively manage resources to achieve the network-level and user-level performance targets such as Quality-of-Service (QoS) requirements, privacy and security constraints, and seamless mobility experience. This project also involves theory building to study the scalability, performance and stability of the learning-based algorithms in large-scale networks with unknowns and imperfections. 


Primary supervisor: Dr Mahsa Derakhshani

Secondary supervisor: Professor Sangarapillai Lambotharan

Find out more

For further project details email Dr Mahsa Derakhshani or register your interest and ask us a question.

Entry Requirements

Applicants should have a 1st class or high 2:1 honours (or equivalent) degree in Electronic Engineering, Computer Science, or a closely related discipline. An MSc with Distinction is desirable. Strong analytical skills, mathematical background, and experience in MATLAB programming are required. Knowledge of wireless communications, optimization techniques, and machine learning algorithms is desirable.

Applicants must meet the minimum English Language requirements, details available on the website.

Fees and funding


Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. University fees and charges can be paid in advance and there are several methods of payment, including online payments and payment by instalment. Special arrangements are made for payments by part-time students.

Please note that these studentships will be awarded on a competitive basis to applicants who have applied to this project and/or the following 30 projects that have been prioritised for funding; job advert ref: WS01 – WS30

If awarded, each 3 year studentship will provide a tax-free stipend of £14,786 p.a ( provisional), plus tuition fees at the UK/EU rate (currently £4,262 p.a). While we welcome applications from non EU nationals, please be advised that due to funding restrictions it will only be possible to fund the tuition fees at the international rate and no stipend will be available. Successful candidates will be notified by 30th April 2018.

How to apply

All applications should be made online.  Under programme name, select Electronic & Electrical Engineering

Please quote reference number: WS19

Application details

Reference number:  WS19
Start date: 01 October 2018
Application deadline: 09 March 2018