About ARU:
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 congestion.
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 analytics.
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.
Candidate requirements:
- The candidate will have or expect to achieve d a postgraduate (Distinction) or equivalent in Computer Science, Engineering, Data Science, Statistics, Maths, or a broad range of relevant backgrounds in Science / Engineering degree.
- Having experiences and knowledge of machine learning, data mining, and artificial intelligence
- 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 similar databases
- Having strong communication and writing skills in English
- Demonstrating confidence in communicating and collaborating with industrial partners
- 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 optimisation models.
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 in practice.
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 restrictions .
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 [email protected] and [email protected] . Please ensure that you make a note of the project title.
For further information, please contact Prof. Ying Xie via [email protected]
We will review all applications after the submission deadline of 24th October 2021. We will contact shortlisted applicants in the week commencing 1st November 2021. Interviews will be held in the week commencing 8th November 2021.
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 community.
Committed to being inclusive and open to discuss flexible working.