Teaching, Research & Scholarship
Fixed term contract
Fixed Term Duration
Faculty of Business & Law
Anglia Ruskin is a vibrant workplace and our University is recognised both
nationally and internationally. We have ambitious plans for the future and we
are determined that our students and staff will realise their full potential.
Our main campuses in the cities of Cambridge, Chelmsford, London and
Peterborough have been transformed with major capital investment. With an
annual turnover of over £200m, we are a major force for higher education and
one of the largest universities in the East of England.
About the role:
The project examines the critical roles that machine learning, big data
analytics and artificial intelligence play in improving yard management in the
Port of Felixstowe. Nowadays, many container ports in the world suffer from
severe congestion. In container ports/terminals, the container yard acts as a
buffer between quayside operations and landside operations. Improving yard
management is essential to enhance operation efficiency and mitigate port
Container ports have generated a massive amount of data from their operations
and communication with business partners. However, there is untapped potential
in using state of the art and advanced analytic techniques to enhance the
existing decision-making processes. In the era of big data analytics, it is
believed that those advanced techniques, such as machine learning, deep
learning and cognitive reasoning, should be able to extract meaningful and
complex data interactions. Machine learning and big data analytics could be
applied to clustering and classifying this data, association rule mining, and
to facilitate the transition to virtualisation, cloud computing and predictive
This project aims to develop a big data driven planning and decision support
system for the yard management at container ports, which will apply machine
learning, data/text mining, time series analysis and optimisation techniques.
The new decision system aims to reduce unproductive crane movements, truck
travel distance, container shuffling, truck turnaround time, and ultimately
improve yard operation productivity. The greenhouse gas (GHG) emissions and
economic benefits resulted from the improved operation productivity will be
proxied from available data sources.
The Port of Felixstowe will be the industrial partner of this project. The
outcomes of this project will be critical for the Port in advancing decision
making in quayside and yard management. It will act as a catalyst to unlock
the potential of the data to support the development of smart ports,
stimulating technological growth and international trade in Felixstowe under
its freeport status.
The candidate will have or expect to achieve d a postgraduate
(Distinction) or equivalent in Computer Science, Engineering, Data
Science, Statistics, Math, or a broad range of relevant backgrounds in
Science / Engineering degree.
Having experiences and knowledge of machine learning, data mining, and
Having strong mathematical or statistical background, with the ability to
construct modelling and simulation
Having strong programming skills using R or Python
Having experience or ability of working with big data stored in SQL or
Having strong communication and writing skills in English
Demonstrating confidence in communicating and collaborating with
Being self-motivated and having a strong interest in doing research
It is desirable that the candidate has a good understanding on shipping
and port management.
Applicants must meet English language requirements, and the project expects an
IELTS of 6.5 in order to be accepted for the PhD programme. Read more here.
The successful candidate will be responsible for data collection from the Port
of Felixstowe and developing machine learning models and stochastic
The successful applicant will work closely with the research centres in
Faculty of Business Law, including the Anglia Ruskin Innovation Centre with
The Welding Institute, and Centre for Intelligent Supply Chain. The PhD
candidate will be supervised by Prof. Ying Xie from Faculty of Business
and Law, Anglia Ruskin University, and Prof. Dongping Song from University
of Liverpool. The Port of Felixstowe will be the industrial partner of
this project, to supervise the design, execution and analysis of models, in
collaboration with academic supervisors. The Port of Felixstowe will also
advise the PhD student and academic supervisors on feasibility of the models
Applicants must be prepared to study on a full-time basis, attending at our
Cambridge or Chelmsford campus, subject to UK Government Covid-19 movement
How to apply:
Applications for the PhD Scholarship are made here. Please choose the course
title “PhD with progression from MPhil School of Economics, Finance and Law”,
Full Time, ARU Chelmsford Campus, R0177FCHE02D, 18/Jan/2022.
In addition to the online application, please send the following documents to
firstname.lastname@example.org and email@example.com . Please ensure that
you make a note of the project title.
Certificates and transcripts from your postgraduate degrees
Your personal statement explaining your suitability for the project
Passport and visa or EU Settlement Scheme share code (if applicable)
Two reference letters
For further information, please contact Prof. Ying Xie via firstname.lastname@example.org
We will review all applications after the submission deadline of 24 th
October 2021. We will contact shortlisted applicants in the week commencing
1 st November 2021. Interviews will be held in the week commencing 8 th
Find out more about working with us.
We offer an extensive range of benefits including a generous holiday
entitlement, occupational pension schemes, training and development
opportunities, travel to work scheme and a competitive relocation package.
Visit our benefits page for full details.
We value diversity at ARU and welcome applications from all sections of the
Committed to being inclusive and open to discuss flexible working.