PhD Studentship, Optimising Machine Learning Algorithms for Industrial Applications

Southampton, United Kingdom
£15,285 tax-free per annum for up to 4 years
02 Dec 2021
End of advertisement period
21 Jan 2022
Contract Type
Fixed Term
Full Time

Operational Research

Location:  Highfield Campus
Closing Date:  Friday 21 January 2022
Reference:  1616821PJ

Project description: 

The aim of this project is to take a very practical approach to machine learning, designing learning algorithms tailored to practical applications using real world data. The plan is to consider various tasks, mostly focused on supervised learning, including classification and regression tasks, developing support vector machines and decision trees-based algorithms, as well as taking advantage of the infrastructure of deep learning methods where necessary. Each of these techniques involves the calculation of one or several hyperparameters, which are crucial for their performance. Hence, the development of the machine learning algorithms expected in this project will take a broad approach, going from the basic training step to the design of powerful hyperparameter algorithms, possibly taking advantage of the hierarchical nature of the hyperparameter optimization problem. The methods to be developed will be driven by applications, as the industrial funding of the project is provided by Decision Analysis Services Ltd (DAS), which has a wide range of clients, from management to highly technical engineering companies. Therefore, algorithms are expected to be tested on a varied base of data sets, from small to very large-scale time series or cross-sectional datatypes.

Industrial sponsor: 

DAS is a fast-growing UK based independent management consultancy firm, with clients across Europe, North America, Africa, and Australasia. DAS’ clients include international infrastructure operators, a range of government departments, local authorities and defence system integrators who look to the company for strategic and operational support for their most critical issues. DAS has expertise in the use of data analytics, systems thinking, programme management and investment modelling in government and business domains. Coupled with extensive regulatory, operations delivery and engineering experience, this expertise underpins DAS’ ability to look deeper into issues, to provide clearer insight and foresight, and to solve critical client challenges. This research will be supported by DAS’ Analytics + Foresight Hub, DAS’ centre of excellence for the delivery of data science, system modelling and futures techniques. 

Informal enquiries:

If you wish to discuss any details of the project informally, please contact Dr Alain Zemkoho, Operational Research Group, School of Mathematical Sciences, Email:,

Entry Requirements:  

First or upper second-class honours degree or equivalent in a discipline with strong quantitative background (e.g., mathematics, computer science, economics, engineering, physics, statistics). 

Other qualifications can be considered in special situations; in these cases, candidates should send full details of their training in mathematics with their application. Even though applicants are assumed to have a background in mathematics and to be competent mathematicians, the project is designed to meet the varying needs of students who have previously studied mathematics as a minor subject within some other discipline.

Closing date:

Applications should be received no later than 21 January 2022, but later applications may be considered until the position is filled. 


The project is fully funded, jointly by DAS and the School of Mathematical Sciences, University of Southampton, and covers full tuition fees at UK rates, and a stipend of £15,285 tax-free per annum for up to 4 years. 

How to apply: 

Applications should be made online (see link below). Select programme type (Research), 2021/22, Faculty of Social Sciences, next page, select “PhD Mathematical Sciences (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Alain Zemkoho.  

Applications should include

  1. A personal statement discussing any experience that you may have around machine learning, optimisation, operational research and/or programming, your mathematical background, and any details of experience that you might have in working with business or industry clients (free form, 1-page A4)
  2. Curriculum vitae - including academic study, work experience and any publications if available
  3. Names and institutional email addresses of two academic referees 
  4. All your degree transcripts to date

Apply online:

For further information please contact:

Host Institution: 

You will be based at the University of Southampton, a research-intensive university, and a founding member of the Russell Group of elite British universities. In the 2014 Research Excellence Framework, Southampton was ranked 8th for research intensity. In 2017-18, Southampton has been ranked 5th in the UK for research grant income. Besides being recognised as one of the leading research universities in the UK, Southampton has also achieved consistently high scores for its teaching and learning activities. In the Research Excellence framework, 100% of Mathematics research impact and research environment was specifically rated as of internationally excellent or world-leading quality. The broad range of Mathematical Sciences at Southampton gives Southampton a unique ability to contribute to the scientific and social challenges facing society. 

Southampton has an excellent track record for optimisation. Statistics and Operational Research groups have existed within Mathematical Sciences since the 1960s. In the early 2000s, the broad multidisciplinary nature of Southampton activity in these areas was recognised through the establishment of the Centre of Operational Research, Management Sciences, and Information Systems (CORMSIS), which spans Mathematical Sciences and Southampton Business School.  Operational Research at the University of Southampton is ranked 33rd in the world, and 6th in the UK, according to the latest QS World Rankings. You will be a member of CORMSIS for the duration of your PhD studies.

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