PhD Funding and Studentships

If you are an eligible postgraduate Doctoral student, you can apply for a loan of up to £25,000 to contribute to overall costs.

Read more about Doctoral loans >>

MRes & PhD (1+4) Programme
PhD (+4) Programme

Alliance Manchester Business School as part of the North-West Social Science Doctoral Training Partnership (NWSSDTP) - a major collaboration between the Universities of Manchester, Lancaster, Liverpool and Keele - is recruiting outstanding candidates for ESRC studentships commencing September 2023 through the ‘Accounting & Finance’, ‘Business & Management’ and ‘Science, Technology & Innovation Policy’ pathways.

NWSSDTP studentships are open to both Home and International candidates (including EU and EEA). Applicants are advised to read the ESRC guidance and information on the NWSSDTP website carefully before applying. From the current competition onwards, three directed studentships will be awarded annually to students from Global Majority communities.

Awards are also available in the following Priority Areas:

• Advanced Quantitative Methods
• Use of ESRC data sets
• Interdisciplinary research, which straddles other research council remits

Criteria

PhD (+4) applicants must have obtained a First or Upper Second Class Honours degree (or equivalent) and hold or expect to obtain a Masters-level qualification with Distinction.
Exceptional Undergraduate students who hold or expect to obtain a First or Upper Second Class Honours degree (or equivalent) may apply to study as 1+4 applicants.

How to apply

To be considered for one of these awards, there is a dual application procedure. Candidates must submit the following:

• Alliance Manchester Business School PhD online application (with supporting documents). Applicants are encouraged to submit their complete application as soon as possible but no later than 9 December 2022

AND

• ESRC NWSSDTP application form via email to HUMS.doctoralacademy.admissions@manchester.ac.uk by 1 February 2023.

However, early applications are strongly recommended.

Enquiries

Details are available on the NWSSDTP website
Contact the Doctoral Academy at HUMS.doctoralacademy.admissions@manchester.ac.uk

The Faculty of Humanities is offering one full-time PhD studentship under the EPSRC Doctoral Training Partnership (DTP) scheme for candidates commencing PhD studies in September 2022. The studentship comprises full-time tuition fees at the (Home) UKRI approved rate, an annual stipend starting at approx. £15,609 and £1,000 per year research training support grant.

Eligibility criteria

1. The award is open to ‘Home’ candidates.

2. Candidates must have an Upper Second Class Honours degree or above and a Masters degree with an overall mark of 60% or above, or an equivalent combination of qualifications and/or experience. Additionally, they are expected to meet the specific requirements of the intended PhD programme.

3. The proposed research project must be within the remit of EPSRC’s research themes as follow:

• Artificial intelligence and robotics
• Digital economy
• Energy
• Engineering
• Global uncertainties
• Healthcare technologies
• Information and communication technologies (ICT)
• Living with environmental change (LWEC)
• Manufacturing the future
• Mathematical sciences
• Physical sciences
• Quantum technologies
• Research infrastructure

Candidates and supervisors should consult the EPSRC website for further details of the eligible research areas and themes.

Selection Process

Alliance Manchester Business School is able to nominate up to two candidates of high calibre whose research projects are aligned with EPSRC research theme(s). Due to the nature of EPSRC’s research priorities, it is expected that the number of nominations in each school will vary according to the relevance of the above themes. Nominations will be scored based on:

1. Proposal quality and research potential
2. Academic track record

How to apply

Candidates should submit an Alliance MBS PhD application and indicate that they wish to be considered for this funding opportunity. If your application is chosen for nomination, you will be asked to provide the following documents, which will form part of the nomination:

1. Research proposal maximum of 1,500 words excluding references. Over-length proposals will be automatically excluded from the Faculty consideration.
2. A separate statement of fit to EPSRC research themes (maximum 250 words)

Application Deadline: 21 February 2022

The RADMA 2023 Doctoral Studies Funding call is now open to fund students from September 2023 onwards.   

The award is focused on supporting students of research technology and innovation management and is closely associated with the R&D Management journal and conferences.  It provides up to three years’ funding to talented prospective or mid-study PhD students.  The expectation is that candidates will have a Master's degree in a relevant field or an excellent first degree or equivalent.  Candidates will be expected to complete yearly progress reports and make a copy of their thesis available to RADMA upon its completion.

Eligibility 

To be eligible for funding, you must be accepted for PhD study at a UK university (see “How to Apply” below).  Applications for the funding must be supported by your PhD supervisor.  Awards will be granted to, and administered through, the institution and not individual candidates.  The award will cover costs related to fees and specified living expenses up to a maximum of £20,109 per academic year (pro-rata for part-time students; partial funding is a possibility) and conditional upon the receipt of acceptable annual progress reports.

For the full call for proposals, RADMA funding application forms, and terms and conditions, visit the RADMA website >>

How to Apply
There is a dual application procedure and you must submit the following:

1. An Alliance MBS PhD application form (with supporting documents) by 9 January 2023. You should indicate in your application that you are applying for RADMA funding. You must also hold an offer for the PhD by 21 February 2023.
2. A RADMA funding application form and Application Spreadsheet (with supporting documents, specified by RADMA) via email to info@radma.net by 28 February 2023.

Enquiries

For queries regarding the PhD programme and application process, please contact the Doctoral Academy: HUMS.doctoralacademy.admissions@manchester.ac.uk

If you have any further questions about this funding, visit the RADMA website or contact RADMA directly at info@radma.net.

This flagship funding scheme will give the most outstanding 'Home' candidates a foundation to support research training with prominent academics across a full range of subjects. You also have the opportunity to:

• Attend a series of exclusive events where you will have the opportunity to meet The University of Manchester President and Vice-Chancellor, interact closely with our academic community and network with other PDS Award students
• Benefit from international research leadership under distinguished scholars
• Become a PDS Award ambassador for the University
• Receive a President's Doctoral Scholar medal
• Benefit from our extensive postgraduate researcher development and training

The Scholarship

These awards offer four years’ funding, including doctoral programme tuition fees and a stipend of approximately £17,668 per year. In addition, the PDS Award entitles you to an additional £1,000 per year enhancement to your stipend.

All researchers receive a generous research support and conference allowance, plus access to:

• A robust doctoral research training programme
• Dedicated research resources (including world-class databases)
• Training in transferable skills
• A visiting speaker seminar programme
• The opportunity to associate with existing research centres and groups.

In addition, all researchers are encouraged to undertake training and development in teaching and deliver teaching and/or research assistantship duties on a paid basis to further enhance their experience in preparation for their future careers.

Entry requirements

Applicants must have obtained a First or Upper Second Class Honours degree (or equivalent) and hold or expect to obtain a Masters level qualification with Distinction. English Language requirements (where required) are IELTS 7.0, TOEFL 623 (100 internet-based test), PTE 76.

In addition, candidates must demonstrate outstanding standards, indicated by one or more of the following:

• Outstanding professional qualification and/or experience;
• Relevant practical or professional achievement/prizes;
• Potential for research output, international collaboration, leadership or public engagement;
• Preparedness for research (e.g. motivation, critical and analytical abilities, technical skills relevant to the proposed research)

Scholarships are awarded on a competitive basis to those applicants with an offer of a place on one of the following PhD programmes for September 2022 entry:

• Accounting and Finance
• Business and Management
• Science, Technology and Innovation Policy

How to apply

To be considered for one of these awards, candidates should submit a PhD application and indicate that they wish to be considered for this funding opportunity. Decisions are based on your academic track record and evidence of your research potential.

Application deadline: 24 February 2023

Candidates are strongly advised to submit their applications as early as possible. Candidates who do not submit the required supporting documents by the deadline will not be considered.

Enquiries

Contact the Doctoral Programmes Office: HUMS.doctoralacademy.admissions@manchester.ac.uk

A full-time PhD research studentship, including full stipend and tuition fee costs, is available for 4 years starting September 2023. The PhD student will conduct research as part of the EPSRC Industrial CASE Award-funded project “Co-op-Solve-M: A Co-operative Algorithm Framework for Solving Large-Scale Heterogeneous Problems with Multiple Objectives”.  

EPSRC Industrial CASE Awards are flagship collaboration projects between industry and academia, aiming to create the research scientists of tomorrow and deliver real value to all stakeholders. This project is only available for UK (Home) students.

The theme of the PhD project:

Rapid developments needed in new energy technologies (e.g. for scalable generation plants that can provide a continuous supply) will depend upon solving linked and large-scale global optimization problems. Whereas algorithms for academic benchmark problems are well-studied, progress in more realistic optimization settings is being stifled by a lack of deep understanding. Most real problems are multi-objective and are composites of several problem types, and what’s needed is a deep dive into how to efficiently solve such problems cooperatively. There are a number of distinct but related research threads that can be brought together to tackle this challenge and make progress. These include using teams of single-objective solvers to cooperatively tackle multiobjective problems; automatic algorithm configuration and selection methods that can combine and tune algorithmic components; and so-called 'interwoven’ problems. Our aim will be to discover common principles among these ideas and bring them together into a new paradigm.

For more details about this project, please see the webpage: https://lopez-ibanez.eu/icase-phd-2023  

Supervision:

The successful candidate will be supervised by Dr. Manuel López-Ibáñez (The University of Manchester). Prof Joshua Knowles (SLB) will act as external supervisor and lead the industrial input into this research. 

The University of Manchester (UoM) has a strong academic track record in AI and Optimization with staff publishing in leading international academic journals and conferences. UoM is also a University Partner of The Alan Turing Institute, the UK's national institute for Data Science and Artificial Intelligence.

Nature of the studentship:

The studentship will commence in September 2023 and will be available for four years, with a stipend equivalent to UKRI rates (the rate was £17,668 tax-free for 2022/23; this is subject to change for future years), plus a top-up stipend from SLB, resulting in an annual stipend of at least £20,500 tax-free. In addition to this stipend, the scholarship covers full university tuition fees, and travel support for fieldwork, conferences and annual visits to SLB.

Entry Requirements:

Applications are sought from talented and motivated UK (Home) candidates with an academic background in at least one of these fields: Computer Science, Mathematics, Statistics, Operations Research, Data Science, Machine Learning, Industrial / Business Engineering or Business Analytics.

Applicants must have a First or Upper Second Class Honours degree (or equivalent) and hold or expect to obtain a Masters level qualification with Distinction.  English Language requirements (where required) are IELTS 7.0, TOEFL 623 (100 ibt), PTE 76.

Application:

Candidates should submit a PhD application for the PhD Business & Management, and indicate that they wish to be considered for the EPSRC/SLB INDUSTRIAL CASE PhD Studentship for “Co-op-Solve-M: A Co-operative Algorithm Framework for Solving Large-Scale Heterogeneous Problems with Multiple Objectives”. 

The application must contain the following: 
•    A research proposal (500-1000 words) related to the topic, and 
•    A written statement clearly indicating how your research competencies and interests to date are aligned with the specific nature of the PhD project.

You are strongly advised to submit your application as early as possible.  If you do not submit the required supporting documents outlined above by the deadline, your application will not be considered.

Application Deadline:

10 May 2023

Enquiries:

If you are interested in this project, please get in touch with Dr. Manuel López-Ibáñez (manuel.lopez-ibanez@manchester.ac.uk) with an up-to-date CV including any publication profile.  

For questions related to your application, contact Lynne Barlow-Cheetham (HUMS.doctoralacademy.admissions@manchester.ac.uk) 

PhD Accounting and Finance
PhD Business and Management
PhD Science, Technology and Innovation Policy

Alliance Manchester Business School (Alliance MBS) is committed to recruiting the highest calibre PhD researchers from across the globe. We are offering a significant number of fully-funded PhD studentships to outstanding candidates wishing to commence their PhD research at Alliance MBS in September 2023.

About Alliance MBS

Inspired by Manchester’s history as a world leader in enterprise, innovation and creativity, Alliance MBS’ research ethos can be defined as “Original Thinking Applied”. We combine cutting-edge and intellectually influential research with a desire to translate this into knowledge and impact on policy and practice.

Alliance MBS is one of a select number of business schools to be triple-accredited by AMBA, AACSB and EQUIS, and features in the world’s leading rankings. These include the 2020 University Impact Ranking, 2021 QS World University Ranking, and 2020 Times Higher Education (THE) World University Ranking.

We are one of the largest and most diverse business schools in the country. The recent £92 million investment into our new building provides state-of-the-art facilities to support our world-leading research. This includes a new data visualisation observatory and behavioural research laboratory.

We are also home to a number of leading research institutes and initiatives, including the Work and Equalities Institute, Manchester Institute of Innovation Research, Sustainable Consumption Institute and a recently launched £32million Productivity Institute. Much of our work is aligned to the wider University’s research beacons (e.g. global inequalities), which means that you will be joining an interdisciplinary community of world-leading scholars.

Our PhD studentships

These PhD studentships offer four years’ funding, including doctoral programme tuition fees and a stipend of approximately £17,668 per year. All researchers receive a generous research support and conference allowance, plus access to:

• A robust doctoral research training programme
• Dedicated research resources (including world-class databases)
• Training in transferable skills
• A visiting speaker seminar programme
• The opportunity to associate with existing research centres and groups.

In addition, all researchers are encouraged to undertake training and development in teaching and deliver teaching and/or research assistantship duties on a paid basis to further enhance their experience in preparation for their future careers.

Applications are sought from exceptional UK, EU and international students with outstanding academic backgrounds and research proposals in the following areas:

PhD Accounting and Finance

• The future of finance and capital markets
• Investment megatrends
• The regulation of the accounting profession, corporate finance and ethical governance
• New insights into audit practice, standard-setting and oversight systems
• The impact of the fintech revolution

PhD Business and Management

• Business practices & employment in the 21st century:
  
  • Improving productivity and living standards
  • Business transformations and work futures
  • Fair treatment at work
  • Inequalities and the life course
  • Regulation and representation
  • Healthy workplaces and wellbeing
• Decision making & accountability in an uncertain world:
  
  • Critical management studies
  • Behavioural science and business decision making
  • Multiple criteria decision analysis and decision support systems
  • The role of data science in new business practices
• Socially responsible innovation and global economic growth:
  
  • International business and management
  • Innovation management, business models and ecosystems
  • Sustainable innovation and systems transitions
  • Operations, project and supply chain management
  • Marketing, including corporate social responsibility, business to business, international marketing and retailing
  • Health innovation management
  • Strategy

PhD Science, Technology and Innovation Policy

• Assessment and governance of emerging technologies
• Responsibility in research and innovation
• Managing and measuring policy
• Knowledge production, innovation, technology deployment, and user engagement
• Roles of universities, public research centres, and other organisations in science and innovation
• Evaluation, foresight and strategic intelligence in science, technology and innovation policy
• How societal, economic and state factors interact to shape the production of scientific knowledge and new technologies
• The generation, diffusion and use of innovations
• The development of socio-technical systems

To discover more about the latest research at Alliance Manchester Business School, view the Alliance Manchester Business School Magazine.

Entry requirements

Applicants must have obtained a First or Upper Second Class Honours degree (or equivalent) and hold or expect to obtain a Masters level qualification with Distinction. English Language requirements (where required) are IELTS 7.0, TOEFL 623 (100 internet-based test), PTE 76.

Scholarships are awarded on a competitive basis to those applicants with an offer of a place on the PhD programme for September 2023 entry.

Candidates are strongly advised to submit their applications as early as possible. Candidates who do not submit the required supporting documents by specific deadlines will not be considered.

Application deadline

24 February 2023

How to apply

To be considered for one of these awards, candidates should submit a PhD application and indicate that they wish to be considered for this funding opportunity.

Enquiries

Contact the Doctoral Academy: HUMS.doctoralacademy.admissions@manchester.ac.uk

Closing Date: 16 January 2023.

The University of Manchester is pleased to continue a formal Agreement with the China Scholarship Council (CSC) to develop research collaborations between UK and Chinese universities/research institutions.

Benefits

• The University of Manchester will cover your full tuition fees for the period of the programme as outlined in the offer letter
• The CSC provides living allowances as prescribed from time to time by the Chinese Government, and covers the round-trip airfare for you from China to the United Kingdom, visa and passport application fees.

If your application is successful, you will be responsible for arranging your visa into the country, and The University of Manchester will not be responsible or liable in any way for a failure to obtain a visa or other requisite paperwork for entry and study in Manchester.

Criteria

• You must be a citizen and permanent resident of the People’s Republic of China at the time of application. If you are an overseas-based Chinese student, you may be eligible subject to CSC policy at the time (CSC publicises the policy in October or November every year)
• If your application is successful you must return to China upon completion of your studies and/or research
• You must hold an unconditional offer letter from The University of Manchester. They must therefore fulfil the relevant academic entry requirements set by Alliance Manchester Business School at The University of Manchester, including a high level of English language proficiency
• Candidates should satisfy the selection criteria set out by CSC on their website.

Application and selection process

• You need to submit a PhD application to The University of Manchester. You need to specify in your application that you wish to apply for The University of Manchester/CSC joint scholarship scheme.
• The University of Manchester will evaluate all applications and rank them in priority order.
• After receiving the offer letter from The University of Manchester, candidates must also apply to CSC for funding by completing an application for funding and employer reference (Application form available online at: https://www.csc.edu.cn/) by the set deadline confirmed by CSC. Please see the above website for the dates of when the CSC application portal will open and close.
• CSC will evaluate all of the applications according to their requirements and priorities, and provide The University of Manchester with a final list of scholarship recipients and inform the successful candidates.

For CSC-related queries, contact: Shemsah Farooq, International Officer, International Development, The University of Manchester (shemsah.farooq@manchester.ac.uk; +44 (0)161 306 3755)

A full-time PhD research studentship, including full stipend and tuition fees, is available for 4 years starting September 2023. The PhD student will conduct research as part of the EPSRC Industrial CASE Award-funded project “Mitigation of Reinforcement Learning Algorithms in Changing Environments”. EPSRC Industrial CASE Awards are flagship collaboration projects between industry and academia, aiming to create the research scientists of tomorrow and deliver real value to all stakeholders.

The theme of the PhD project

The development of (deep) Reinforcement Learning (RL) algorithms to train agents within game environments is well known. Agent training is typically conducted against a known, simplified, or constrained environment. However, the deployed environment is typically more complex and subject to some change and uncertainly not represented in the training environment.

RL algorithms typically characterise performance against probabilistic arenas, rather than being able to cope with an environment subject to change over time. The performance of the resulting RL agent can then be expected to become compromised over time, but not necessarily be catastrophic.

In this PhD project, we are concerned with (i) understanding this performance degradation and (ii) the development of mitigating strategies. More specifically, the project will focus on creating a train-and-test framework comprising a simulation engine for a dynamic environment and a configurable RL approach. In addition to considering changes in the environment, the simulator and RL agent will need to account for real-world challenges, such as multiple conflicting objectives, robustness, and safety issues.

The team at BAE Systems is focused on cutting-edge research in advanced simulation, optimization, and machine learning, and is thus invested in how RL can be extended to support decision-making in dynamic environments. The project will therefore contribute directly to BAE Systems’ ongoing research. From a scientific perspective, this project will lead to cross-disciplinary research and output that is of high quality and significance.

Due to the nature of this topic, candidates may be subject to a security check.

Supervision

The successful candidate will be supervised by Professor Richard Allmendinger (Alliance MBS), Dr Theodore Papamarkou (Mathematics) and Dr Wei Pan (Computer Science), The University of Manchester. Simon Mettrick and Markus Deittert from BAE Systems will act as external supervisors and lead the industrial input into this research.

The University of Manchester (UoM) has a strong academic track record in AI and Optimization with staff publishing in leading international academic journals and conferences. UoM is also a University Partner of The Alan Turing Institute, the UK's national institute for Data Science and Artificial Intelligence.

Nature of the studentship

The studentship will commence in September 2023 and will be available for four years, with a stipend equivalent to UKRI rates (the rate was £16,062 tax-free for 2022/23; this is subject to change for future years), plus an industrial top-up stipend from BAE Systems, subject to contract. This also covers full university tuition fees, and travel support for fieldwork, conferences and annual visits to BAE Systems. This project is available for UK students but we are able to offer a limited number of scholarships that will enable full studentships to be awarded to European applicants.

Entry requirements

Applications are sought from talented and motivated Home and European candidates with an academic background in at least one of these fields: (Deep/multi-agent) reinforcement learning, deep learning, Gaussian processes, Bayesian optimization, transfer/online/meta-learning/safe/multitask learning, dynamic control.

Applicants must have a First or Upper Second Class Honours degree (or equivalent) and hold or expect to obtain a Master's level qualification with Distinction. English Language requirements (where required) are IELTS 7.0, TOEFL 623 (100 ibt), PTE 76.

Application

Candidates should submit a PhD application for the PhD Business & Management, and indicate that they wish to be considered for the EPSRC/BAE Systems INDUSTRIAL CASE PhD Studentship for “Mitigation of Reinforcement Learning Algorithms in Changing Environments”.

The application must contain the following:

• A research proposal (500-1000 words) related to the topic, and
• A written statement clearly indicating how your research competencies and interests to date are aligned with the specific nature of the PhD projects.

You are strongly advised to submit your application as early as possible. If you do not submit the required supporting documents outlined above by the deadline, your application will not be considered.

Application deadline

10 May 2023

Enquiries

If you are interested in this project, please contact Professor Richard Allmendinger at richard.allmendinger@manchester.ac.uk with an up-to-date CV including any publication profile.

For questions related to your application, contact the Doctoral Academy at HUMS.doctoralacademy.admissions@manchester.ac.uk

Alliance Manchester Business School, in conjunction with The Productivity Institute, is announcing a new scholarship to support PhD students whose work is in the area of productivity.

Alliance Manchester Business School is committed to recruiting the highest calibre PhD students from across the globe. With an excellent international reputation, we strive to produce graduates and researchers of distinction who attain the highest standards of academic excellence, contribute to their disciplines and typify Original Thinking Applied.

The scholarship is to support PhD research into all aspects of the productivity performance of firms, sectors, regions, and countries. The research is in collaboration with The Productivity Institute, a major research initiative financed by the UK Economic and Social Research Council.

The Scholarship

This PhD scholarship offers 4 years’ funding, including doctoral programme tuition fees and a stipend of approximately £17,668 per year for candidates wishing to commence their studies in September 2022. The successful candidate will also receive a generous research support and conference allowance. You will also have access to a robust doctoral research training programme, dedicated research resources, training in transferable skills, visiting speaker seminar programme, and associate with existing research centres and groups. In addition, all students are encouraged to undertake training and development in teaching and deliver teaching and/or research assistantship duties on a paid basis to further enhance their experience in preparation for their future careers.

Research Area

Planned research should relate to the productivity performance of developed economies and to the research areas of The Productivity Institute. This covers firm performance, skills and human capital, finance and investment, macroeconomics, regional and international comparisons, public policy, and the productivity challenges created by the new technologies and the transition to net-zero. The Productivity Institute is multi-disciplinary and welcomes applications from business studies, economics, and related social sciences.

Supervision

Successful candidates will be supervised by an appropriate team drawn from the membership of the Productivity Institute and its partner institutions including The University of Manchester (the degree-awarding institution).

Entry Requirements

Applications for this project are sought from exceptional UK, EU and international students with an outstanding academic background.

Applicants must have a First or Upper Second Class Honours degree (or equivalent) and hold or expect to obtain a Master's level qualification with Distinction. English Language requirements (where required) are IELTS 7.0, TOEFL 623 (100 ibt), or PTE 76.

How to apply

Candidates should submit a PhD application for the full-time PhD Business & Management and indicate that they wish to be considered for this particular funding opportunity. Your PhD topic will be as outlined in your research proposal.

Candidates who have already applied for admission to this PhD programme may, if their research meets the criteria above, signal that they wish to be considered for this scholarship by contacting the Doctoral Academy at HUMS.doctoralacademy.admissions@manchester.ac.uk, quoting their current University of Manchester ID and Application number.

Your application must contain a 3000-word research proposal related to the topic.

Candidates are strongly advised to submit their application as early as possible. Candidates who do not submit the required supporting documents by the specified deadline will not be considered.

Application deadline

19 July 2023

Enquiries

For questions related to this opportunity or making your application, please contact the Doctoral Academy at HUMS.doctoralacademy.admissions@manchester.ac.uk

About the Project

Cricket is considered the world’s second most popular sport. The plethora of available cricket data and the development of AI technologies have created a massive demand for cricket data analytics. The applications of AI in the cricket domain have increased dramatically during the last two decades.

This PhD project is about developing and validating customized machine learning and data analytics methods for mining cricket-related data to support decision-making in talent nurturing and performance profiling.

The project will be done in collaboration with the England and Wales Cricket Board (ECB), which is the national governing body of cricket in England and Wales. ECB will also provide access to several years of detailed national and international cricket match data, (longitudinal) performance data of cricket players, and scouting data. The data available to hand is of huge volume, and hence another question to be answered by the successful student is which of the many datasets to use, from which periods, and at what granularity.

This project will focus on two main questions/research gaps:

1. What are the driving factors for the development of England’s cricket players?

Addressing this question will involve using statistical analysis and machine learning (e.g. feature importance, dimensionality reduction, clustering) to explore the demographic and developmental sporting history of different player roles (e.g. pace bowlers, batters and wicket keepers) with differing expertise levels to assess the impact these factors have on the development of England’s players. Having an improved understanding (that is backed up by the data) of the driving factors, will be useful for nurturing talent and customising training regimes.

2. What data and performance metrics can be used to predict success for different formats of cricket (e.g. Test, ODI, iT20)?

Addressing this question will involve using feature engineering and prediction methods to identify performance metrics in the First Class (county) game that predict success at International level in Test, One Day International and T20I) formats for English players (potential to explore overseas players). Ultimately, one may want to influence the driving factors for development (Q1) according to the performance metrics identified in Q2 to provide cricket players with a personalized career path.

The successful student would be able to develop the research programme within the scope of the two broad topic areas.

The project will be done in collaboration with the England and Wales Cricket Board (ECB), the national governing body of cricket in England and Wales. The board oversees all levels of cricket in England and Wales, including the national teams: England Men (Test, One Day International and T20I), England Women, England Lions (Men’s second tier), Physical Disability, Learning Disability, Visually Impaired, and Deaf.

ECB will also provide access to several years of detailed national and international cricket match data, (longitudinal) performance data of cricket players and scouting data. The data available to hand is of huge volume, and hence another question to be answered by the successful student is which of the many datasets to use, from which periods, and at what granularity.

For queries regarding the project, please email Richard Allmendinger at richard.allmendinger@manchester.ac.uk

Application deadline

Monday, 10 April 2023

Funding

A standard studentship covers the cost of tuition fees, provides a doctoral stipend of £17,668, (to increase slightly each year with inflation), and a Research Training Support Grant, for four years. Applications are open to both Home and International fee-rated applicants.

Entry requirements

2:1 honours degree, or equivalent, in a relevant subject area
English language requirements (for international/EU candidates)

You must have one or more of the following:

• IELTS (International English Language Testing System) – 7 overall, 7 writing, 6 other sections
• TOEFL (Test of English as a Foreign Language) Internet-based test – 100 overall, 25 writing, 22 other sections
• A degree in any subject completed in the English language from a majority English-speaking country may also be acceptable – please check the entry requirements for the country where you completed your degree.

How to Apply

To apply please complete an online application form. Please note – you must apply through the Leeds portal, even if you wish to study at Manchester. Do not apply directly to Manchester.

Supporting Documents: Please check the entry criteria and application information to ensure you include the required documents with your application form.

We have funds available to help with accessibility and widening participation, please get in touch for details.

About the Project

Mining produces tailings which are rejects or resultant waste streams after performing the process of separating the valuable fraction from the uneconomic fraction of an ore. It was a widespread practice that these tailings were not monitored. After mine closure, tailings have often been left to their own. Consequently, elements like Arsenic, Lead or Copper are washed out over time and bear the risk of groundwater pollution, soil contamination and other environmental damage.

To mitigate these dangers worldwide, tailings need to be analysed and potentially reprocessed – a highly topical issue that accompanies the mining industry today and in the coming decades. The United Nations and business communities developed the first international standards for tailings management in 2020 after a deadly tailing disaster in Brazil. Besides the need to avoid dangers to society, it is often financially viable to pursue reprocessing as valuable minerals can be extracted from the tailings with modern and clean extraction methods.

The fast and accurate measurement of tailings for the determination of mineralogies is a basic requirement for the successful accomplishment of this task. The classical way of tailings investigation is based on samples from holes drilled into the tailings' bodies; these samples are then analysed in the laboratory. This is an expensive and complex process. The company involved in this project, J&C Bachmann, has realised a push probe technique that analyses materials in the tailings without extraction. This technique drastically reduces the cost of tailings analysis and makes it more viable for governments and companies to progress on the issue and thus speeds up the action where required.

The push probe is equipped with spectrometers for X-ray and infrared radiation. X-ray and infrared spectra complement each other ideally. The dimensionality of the data is high given the complexity related to the analysis. We expect that data fusion and machine learning techniques will allow a broad band of mineralogies to be quantified with high accuracy. J&C Bachmann will provide real-world measurement data from measurement campaigns as well as lab analyses. The high data quantity requirements for machine learning are planned to be fulfilled by simulation models with standard industry tools, and inter- and extrapolation techniques.

Research objectives

• Simulate new spectrometer information using industry-standard simulation tools (e.g., Geant4).
• Develop and train machine learning-based classification models on real-world and simulated data from spectrometers to determine and quantify mineralogies.
• Fuse spectrometer information to a data set and train an advanced machine learning model on the fused data to determine and quantify mineralogies.
• Compare the standard methodology for mineralogy determination with the results from machine learning models.
• Evaluate the dependency of the model on specific parameters of the tailing data used.

Research questions

• Which of the tailings can be reliably determined and quantified using X-ray and infrared spectra and how accurately?
• How can the available data be multiplied using simulation models and extrapolation techniques to generate a significant amount of data required for developing advanced machine learning models?
• Does fusion of the sensor data of measurement instruments allow a more comprehensive and reliable determination of the mineralogies than the simultaneous and independent use of instruments?
• How can the information be transferred into a mineralogic model of tailing to allow the neutralisation of poisonous elements in the tailing with minimal ecological impact?
• How to transfer and adapt machine-learning models developed for specific kinds of tailings (e.g. coal tailings) to other commodities (e.g. copper tailings)?

The academic supervisory team includes Praduymn Shukla (pradyumn.shukla@manchester.ac.uk) and Arijit De (arijit.de@manchester.ac.uk), both based at Alliance Manchester Business School).

Application deadline

18 June 2023

Funding

A standard studentship covers the cost of tuition fees, provides a doctoral stipend of £17,668, (to increase slightly each year with inflation), and a Research Training Support Grant, for four years. Applications are open to both Home and International fee-rated applicants.

Entry requirements

2:1 honours degree, or equivalent, in a relevant subject area

English language requirements (for international/EU candidates)

You must have one or more of the following:

• IELTS (International English Language Testing System) – 7 overall, 7 writing, 6 other sections
• TOEFL (Test of English as a Foreign Language) Internet-based test – 100 overall, 25 writing, 22 other sections
• A degree in any subject completed in the English language from a majority English-speaking country may also be acceptable – please check the entry requirements for the country where you completed your degree.

How to Apply

To apply please complete an online application form. Please note – you must apply through the Leeds portal, even if you wish to study at Manchester. Do not apply directly to Manchester.

Supporting Documents: Please check the entry criteria and application information to ensure you include the required documents with your application form.

We have funds available to help with accessibility and widening participation, please get in touch for details.

About the Project

Emissions of greenhouse pollutants have been the central point of climate change policies, being the central policy measure of global treaties such as the Paris Accord (UN, n.a.). One of the most interesting concepts designed within the Kyoto Protocol to tackle greenhouse gas (GHG) emissions was the creation of a trading system for carbon emissions where one carbon unit (a tonne) would be one credit and big pollutant entities (companies, countries) would have to obtain credits to emit carbon and greener ones would sell their carbon savings. Carbon markets have since the late 1990s been in place spanning from countries, for example, the New Zealand Emissions Unit Register (NZG, 2008) to continents with the EU Emissions Trading Scheme (EU Commission, 2005).

These markets operate at a very aggregate level of emissions, with only large polluters (e.g. aviation companies, energy companies or individual power stations, cement producers or countries) trading credits. Individuals can indirectly participate in the systems in multiple commercial or charity schemes of offsetting their carbon emissions with operators that eventually will trade these credits in the current trading schemes. These offset schemes have been under strong scrutiny due to the difficulty of auditing their efficiency (The Guardian, n.a.) and the risk of greenwashing individual attitudes. On the other hand, the role of individual behavioural change towards more sustainable attitudes is central to any climate change adaptation policy, with communities and political discourses and media attention highlighting it.

There is a revival of interest in developing personal or individual carbon credit systems. There is some research on setting the principles and designing possible systems (Fuso Nerini et al., 2021) and some older analyses of principles and bottlenecks (Fawcett and Parag, 2010). On the policy side, there are few initiatives. For the UK contemplates this possibility since the Climate Change Act 2008, without notice of further implementation. Many of these systems are also focused on specific domains in isolation, such as house energy or transport.

With current data, machine learning and simulation methods and techniques there are many more possibilities to explore the complexities of all the domains in which carbon emissions of individuals take place, and how can they be saved to create carbon credits. This can be coupled with simulation, visualisation and gamification methods and tools to create powerful and usable systems that are more effective in inducing behavioural change and supporting climate adaptation policies.

The aim of this project will be to explore theoretical concepts and methodological approaches to create an integrated, multidomain (e.g. transport, housing, consumer patterns, finance, indirect emissions), and high-resolution carbon credit system (CCS) for individuals. This will include four main components:

(1) conceptualising a new model of carbon credits, co-designed with relevant stakeholders (e.g. urban decision-makers, transport operators, consumers groups, local community groups) suitable for accounting for individual emissions and savings, for different domains of human activities, that can be measured in an accurate, transparent and untransmissible way;

(2) develop a system to compile, process and visualise data to support computing accurate measures of generation and saving of carbon emissions at the individual level, to generate new, interoperable datasets that can be used by different assessment, simulation and gamification tools;

(3) develop multisystem assessment and simulation tools that can help individuals understand in detail their environmental footprint, inducing effective individual behavioural change that contributes to collective sustainability, with clear implications in both the local (neighbourhood) and the city contexts;

(4) develop a policy framework where these individual carbon credits can be used as policy tools to generate effective bottom-up approaches to more sustainable management of aggregate systems that already trade in the carbon credit markets such as transport systems or cities.

Data Sources

This project will make use of existing open-access numerical and descriptive data about consumer behaviours (e.g., retail, finance, leisure, public services), housing characteristics and travel behaviours. The project may use area data (e.g. national censuses, deprivation) to characterise neighbourhoods and cross-analyse it with individual data, using estimation models that may be able to complement missing variables in the datasets. The project will also use pilot schemes where new types of sensors are being used to generate accurate carbon emissions, for example in public transport vehicles.

The project will be developed in collaboration with two industry partners which possess expertise in the field.

Modeshift (USA, Bulgaria) has expertise in developing data collection and management systems for public transport operators where carbon emissions are measured accurately for each travel (and not only based on generic vehicle specs). Modeshift participates in international projects and in policy initiatives (for example at the European Union level) to design new policy tools to account for individual carbon credits.

EarthChain (UK) has expertise in developing systems to account for and certify carbon emissions using blockchain technology for retail, consumer goods and banking, having experience in working on improving the accuracy of the carbon footprint of supermarkets and other consumer goods and in travel emissions.

The academic supervisory team includes Nuno Pinto (nuno.pinto@manchester.ac.uk) and James Evans (James.Z.Evans@manchester.ac.uk), both from the School of Environment, Education and Development. For queries regarding the project, please email the supervisors directly.

Application deadline

Monday, 10 April 2023

Funding

A standard studentship covers the cost of tuition fees, provides a doctoral stipend of £17,668, (to increase slightly each year with inflation), and a Research Training Support Grant, for four years. Applications are open to both Home and International fee-rated applicants.

Entry requirements

2:1 honours degree, or equivalent, in a relevant subject area.

English language requirements (for international/EU candidates)

You must have one or more of the following:

• IELTS (International English Language Testing System) – 7 overall, 7 writing, 6 other sections
• TOEFL (Test of English as a Foreign Language) Internet-based test – 100 overall, 25 writing, 22 other sections
• A degree in any subject completed in the English language from a majority English-speaking country may also be acceptable – please check the entry requirements for the country where you completed your degree.

How to Apply

To apply please complete an online application form. Please note – you must apply through the Leeds portal, even if you wish to study at Manchester. Do not apply directly to Manchester.

Supporting Documents: Please check the entry criteria and application information to ensure you include the required documents with your application form.

We have funds available to help with accessibility and widening participation, please get in touch for details.

About the Project

This PhD project is about developing and validating advanced Bayesian optimization algorithms to drive the search for biopharmaceutical manufacturing processes that are more efficient in terms of costs, environmental impact, floorspace usage, etc. This means that drugs will be more accessible and cheaper for patients while affecting the environment less.

The industry partner of this project, Biopharm Services, has developed a market-leading computational tool called BioSolve to support drug manufacturers and suppliers in their quest to design more economical and sustainable production processes. BioSolve comprises a detailed Excel-based process economics-mass balance model that can be configured to create digital twins of different biopharmaceutical production processes. An initial Bayesian Optimizer (BO) has been connected to BioSolve, and the goal of this PhD project is to bring this optimizer to a new level.

Biopharmaceuticals are pharmaceutical drug products manufactured in, extracted from, or semi-synthesized from biological sources. They include, for example, vaccines, gene therapies, and recombinant therapeutic proteins. Although very effective and accurate, important issues of concern are the cost of production of biopharmaceuticals (e.g. expensive resources, need for high purity), the environmental impact of some of the emerging production technologies (e.g. disposables), and the space needed for the production sites.

The focus of the project is to advance the BO’s capabilities in terms of at least one of the following problem properties:

• Quantifying, simulating and accounting for the inherent noise in a drug production process.
• Tackling problems or larger scale in the decision and/or objective space.
• Allowing users to perform interactive multi-objective optimization.

All of these three capabilities will require methodological contributions, which can then be validated on real use cases provided by the industry partner (using also the BioSolve simulator) or on synthetic test problems from the global optimization literature (which may need to be modified to account for the various problem features).

The research questions to be tackled in the scope of the project are:

• Can we develop a (heterogeneous) noise-handling method (e.g. based on state-of-the-art uncertainty quantification methods) for BO that is able to deal with different types and degrees of noise across the multiple objectives?
• Can we develop a Bayesian multi-objective optimizer (e.g. based on decomposition-based methods) able to solve problems with many mixed-type decision variables (between 20-40 variables of varying type) and many objectives (more than 2)?
• How do we combine the methods developed in 1) and 2), and use them within an interactive optimization framework to guide the search towards a user-preferred part of the search space only?
• What existing synthetic test problems can be used / how do we combine existing synthetic test problems to test the different capabilities of the developed BO?
• Does the developed BO perform robustly on real biopharmaceutical production case studies provided by the industry partner (with BioSolve being used as the evaluation engine)?

These are just some examples – the successful student would be able to develop the research programme within the scope of the broad topic area.

The academic supervisory team includes Richard Allmendinger (richard.allmendinger@manchester) from Alliance Manchester Business School and Mauricio Alvarez (mauricio.alvarezlopez@manchester.ac.uk) from the Department of Computer Science. For queries regarding the project, please email the supervisors directly. 

Application deadline

Monday, 10 April 2023

Funding

A standard studentship covers the cost of tuition fees, provides a doctoral stipend of £17,668, (to increase slightly each year with inflation), and a Research Training Support Grant, for four years. Applications are open to both Home and International fee-rated applicants.

Entry requirements

2:1 honours degree, or equivalent, in a relevant subject area.

English language requirements (for international/EU candidates)

You must have one or more of the following:

• IELTS (International English Language Testing System) – 7 overall, 7 writing, 6 other sections
• TOEFL (Test of English as a Foreign Language) Internet-based test – 100 overall, 25 writing, 22 other sections
• A degree in any subject completed in the English language from a majority English-speaking country may also be acceptable – please check the entry requirements for the country where you completed your degree.

How to Apply

To apply please complete an online application form. Please note – you must apply through the Leeds portal, even if you wish to study at Manchester. Do not apply directly to Manchester.

Supporting Documents: Please check the entry criteria and application information to ensure you include the required documents with your application form.

We have funds available to help with accessibility and widening participation, please get in touch for details.

About the Project

Across the UK, buses provide vital access and mobility for both rural communities, particularly low-income groups, commuters and the disabled, and provide a means to cut congestion and carbon emissions. However, fragmented, and lower-density populations make the provision of bus services in remote rural areas more challenging (Campaign for Better Transport, 2018; Department for Transport, 2021). Many services run with few passengers and their existence is financially precarious, especially as passenger numbers on buses remain at approximately 70% of the pre-pandemic level (Department for Transport, 2022). Identifying new sources of income to improve the financial viability of bus services is an important industry challenge (Campaign for Better Transport, 2018). The COVID-19 pandemic and economic climate (especially high inflation) have placed an existential threat on many rural bus routes that are vital for the communities they serve.

This is a significant problem for many local authorities throughout the UK and therefore finding ways of reducing or offsetting operating costs (for example, through the carriage of cargo as well as passengers) is an important research and practical challenge.

A mass of small-scale businesses characterises rural economies (Phillipson et al., 2019), often distributing their goods to buyers themselves, with transport and logistics a substantial cost financially and in terms of staff time. For instance, food and drink industry producers typically undertake regular deliveries to local and regional buyers but many of these journeys involve small loads. Improving the efficiency of logistics can improve business performance and reduce carbon emissions (De et al., 2022; De;Tocco and Gorton, 2022) but requires greater cooperation with other actors for mutual benefit (De;Tocco and Gorton, 2022).

Cargo buses carry both passengers and goods on regular services (PortCalls, 2017), and they can utilize existing bus vehicles but with dedicated space for the transport of goods.

Project aims:

1. Map the logistics needs, and deliveries, of small-scale producers in remote rural areas of England against existing bus routes and timetables to identify the most promising opportunities for cooperation and establishment of cargo bus routes. This will involve working with Food and Drink North East (FADNE) and its members.
2. To investigate the practical and legal requirements for the establishment of cargo buses. This will involve liaison with business service operators, local authorities and transport authorities. Attention will also be paid to costs and willingness to pay for cargo bus services.
3. To model the potential financial benefits and costs for small-scale producers across England of adopting passenger buses for cargo shipments and examine the impact on carbon footprints.
4. To undertake a small number of test cargo journeys to establish the proof of concept, and to provide a basis for future service development and commercialisation.

Research questions:

1. Can we develop a robust machine learning model for forecasting the order amount from various small-scale producers in remote rural areas of England while considering the historical customer demand for various products?
2. Can we propose a machine learning model for estimating the available cargo spaces on the passenger buses running from remote rural areas to nearby towns and cities during the off-peak and peak hours while considering the information about the number of passengers boarding the buses during different times of the day?
3. How can we propose a robust optimisation model for delivery of the ordered amount from small-scale producers while considering the available cargo spaces on the existing bus routes and timetables for passenger bus services from remote rural areas to nearby towns/cities?
4. Can we develop a multiple objective optimisation model aiming to minimise the transport cost, fuel cost, carbon emissions from passenger bus services, and travel time for delivering products, while maximising the profitability of the bus services, the profitability of remote rural producers and vehicle space utilisation?

Partner contributions to the project:

Northumberland County Council (NCC) will contribute to the CDT PhD Project by connecting the logistics modelling research with its end-users operating across multiple sectors in the rural economy. This project is of interest to NCC as a way of improving air quality and the financial viability of rural bus services by optimising vehicle utilisation (especially during off-peak periods when there is often spare capacity available) and reducing carbon emissions in remote rural areas. NCC will support the development, scope, and ambition of the planned work, providing advice to the research team, facilitating stakeholder connections, knowledge exchange services and promotional activity.

The National Innovation Centre for Rural Enterprise (NICRE) and Food and Drink North East (FADNE) will contribute to the CDT PhD Project by providing a physical base for the successful PhD student to undertake fieldwork and interact with stakeholder partners in the northeast of England. Furthermore, NICRE would give advice to the PhD student on rural economy aspects of the project and help manage relationships with Northumberland County Council (NCC), Food and Drink North East (FADNE) and other regional stakeholders. NICRE and FADNE would also help the PhD project by providing access to rural businesses and facilitating practical tests to achieve the proof of concept.

For queries regarding the project, please email Arijit De at arijit.de@manchester.ac.uk.

Application deadline

Monday, 10 April 2023

Funding

A standard studentship covers the cost of tuition fees, provides a doctoral stipend of £17,668, (to increase slightly each year with inflation), and a Research Training Support Grant, for four years. Applications are open to both Home and International fee-rated applicants.

Entry requirements

2:1 honours degree, or equivalent, in a relevant subject area.

English language requirements (for international/EU candidates)

You must have one or more of the following:

• IELTS (International English Language Testing System) – 7 overall, 7 writing, 6 other sections
• TOEFL (Test of English as a Foreign Language) Internet-based test – 100 overall, 25 writing, 22 other sections
• A degree in any subject completed in the English language from a majority English-speaking country may also be acceptable – please check the entry requirements for the country where you completed your degree.

How to Apply

To apply please complete an online application form. Please note – you must apply through the Leeds portal, even if you wish to study at Manchester. Do not apply directly to Manchester.

Supporting Documents: Please check the entry criteria and application information to ensure you include the required documents with your application form.

We have funds available to help with accessibility and widening participation, please get in touch for details.

About the Project

Primary Care General Practice (GP) services in England are under pressure as never before, with an ageing population, the COVID pandemic, and increasing numbers of patients with complex multi-morbidities meaning that services are facing significant pressures. NHS England is expecting primary care services to do more for less whilst ensuring the delivery of personalised care and ensuring the best outcomes for patients. GPs collect large amounts of patient appointment data. However, this data is not currently used in a systematic and structured manner for planning purposes. As a result, appointment times are often either under or over-estimated, resources are misallocated and uncertainty around cancellations is not properly managed, leading to suboptimal planning, poor prioritisation, and long waiting times.

The overall goal of this project is to improve GP appointment scheduling across all of NHS England by helping GPs to make data-driven planning decisions. In addition to the obvious societal impact, the models, algorithms, and case studies will be published in top operation research journals, such as Management Science, the European Journal of Operational Research and the Journal of Operations Management.

The aim of this research is to devise GP appointment planning policies for dealing with waiting lists and uncertainty of events The project will develop mathematical models and algorithms to evaluate policies and produce optimal scheduling in terms of priority, sequencing, length of appointment and practitioner/nurse. In particular, the main decisions include determining the optimal number of GPs, nurses and required equipment to attend to patients within approved waiting time standards. The complicating element of this problem is uncertainty in visit duration and number/types of visits. Existing works in the literature (Kuiper & Lee 2022; Marynissen & Demeulemeester, 2019) either lack incorporating key factors such as uncertainty of data or are not able to solve real size problems to optimality. Moreover, the focus of the literature is mainly on operating room scheduling and significantly less attention has been paid to primary care.

Optimal allocation of staff and resources is one of the key factors contributing towards improving health inequality, particularly geography inequality. To this end, an optimisation model will be developed to find the optimal level of resources which may include GPs, nurses, rooms, and equipment.

Research questions

1. What are the key components that have a major impact on planning for primary care?
2. What are uncertain parameters and what are their characteristics i.e., what are the major factors impacting uncertain parameters? And how to deal with uncertainty?
3. How do these components interact with each other? How can they be mathematically modelled?
4. How can the problem be mathematically formulated as a whole?
5. Can it be efficiently reformulated within the simulation-optimisation framework?
6. What is the most efficient solution method to solve the resulting problem?

This project is a collaboration with Midlands & Lancashire Commissioning Support Unit (MLCSU). MLCSU Business Intelligence and Analytical service provides analytical and data management services to Integrated Care Boards across the Midlands and North-West of England. MLCSU will provide their expertise to guide the formulation of objectives and constraints as well as the evaluation of prototypes produced by the project. MLCSU will also provide support in data collection, cleaning and data governance.

The academic supervisory team includes Manuel Lopez-Ibanez (manuel.lopez-ibanez@manchester.ac.uk) and Christopher Smith (Christopher.Smith@manchester.ac.uk), both from Alliance Manchester Business School. For queries regarding the project, please email the supervisors directly. 

Application deadline

18 June 2023

Funding

A standard studentship covers the cost of tuition fees, provides a doctoral stipend of £17,668, (to increase slightly each year with inflation), and a Research Training Support Grant, for four years. Applications are open to both Home and International fee-rated applicants.

Entry requirements

2:1 honours degree, or equivalent, in a relevant subject area.

English language requirements (for international/EU candidates)

You must have one or more of the following:

• IELTS (International English Language Testing System) – 7 overall, 7 writing, 6 other sections
• TOEFL (Test of English as a Foreign Language) Internet-based test – 100 overall, 25 writing, 22 other sections
• A degree in any subject completed in the English language from a majority English-speaking country may also be acceptable – please check the entry requirements for the country where you completed your degree.

How to Apply

To apply please complete an online application form. Please note – you must apply through the Leeds portal, even if you wish to study at Manchester. Do not apply directly to Manchester.

Supporting Documents: Please check the entry criteria and application information to ensure you include the required documents with your application form.

We have funds available to help with accessibility and widening participation, please get in touch for details.