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EPSRC/IBM INDUSTRIAL CASE PhD Studentship in Data-Driven Optimization: Tuning Bayesian Optimization for Problems with Dynamic Resource Constraints

  • Full or part time
  • Application Deadline
    Friday, April 17, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

A full-time PhD research studentship, including full stipend and tuition fee costs, is available for 4 years starting September 2020. The PhD student will conduct research as part of the EPSRC Industrial CASE Award funded project “Tuning Bayesian Optimization for Problems with Dynamic Resource Constraints”. 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.

Theme of the PhD project

Closed-loop optimization deals with problems in which candidate solutions are evaluated by conducting experiments, e.g. physical or biochemical experiments. Although this form of optimization is becoming more popular across the sciences, it may be subject to rather unexplored resourcing issues, as any experiment may require resources in order to be conducted. In this PhD project we are concerned with (i) understanding how Bayesian optimization is affected by dynamic resource constraints – a type of constraint that models the availability of resources (e.g. raw materials, storage, computing power and storage, skilled engineers, equipment and machines, budget, etc) required to conduct a physical experiment or run a time-consuming simulation – and (ii) the development of search strategies to tackle this particular problem issue.

The team at IBM Research are focused on cutting-edge research in advanced Bayesian modelling, including Bayesian Optimization, and are thus invested in how Bayesian Optimization can be extended to challenging optimization tasks. The project will therefore contribute directly to IBM’s ongoing research, with the opportunity for influencing future IBM products. From a scientific perspective, this project will lead to cross-disciplinary research and output that is of high quality and significance.


The successful candidate will be supervised by academics at the Management Sciences and Marketing Division at Alliance Manchester Business School: Dr Richard Allmendinger, Dr Manuel Lopez-Ibanez, Dr Jonathan Shapiro, Prof Joshua Knowles. Dr Matt Benatan, Algorithms Subgroup Lead in Machine Learning and AI, from IBM Research UK, will act as external supervisor and lead the industrial input into this research.

The Management Sciences group hosts a number of leading world-class academics with an outstanding reputation for excellence in research and teaching programmes at all levels. The group has a superb research track record, with staff regularly publishing in leading international academic journals and conferences. The group maintains a deep commitment to knowledge development and collaborative research, and has long-standing connections with the business and public sector organisations.

Nature of the studentship

The studentship will commence in September 2020 and will be available for up to four years, with a stipend equivalent to UKRI rates (approx. £15,285 tax free for 2020/21 and subject to change for future years), plus an industrial top-up stipend from IBM, subject to contract. This also covers full university tuition fees (at Home/EU level), and travel support for fieldwork, conferences and annual visits to IBM.

Entry Requirements

Applications for this project are sought from exceptional UK and EU candidates with an outstanding academic background in the fields of machine learning (in particular Gaussian processes) or Bayesian/data-driven/dynamic optimization. You must demonstrate that you are highly motivated, intellectually astute, with high-level analytical competencies and development skills.

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 66.


Candidates should submit a PhD application for the PhD Business & Management by 17th of April 2020, and indicate that they wish to be considered for the EPSRC/IBM INDUSTRIAL CASE PhD Studentship “Tuning Bayesian Optimization for Problems with Dynamic Resource Constraints”.

The application must contain the following:
• A 3000-word research proposal 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.


If you are interested in this project, please contact Dr Richard Allmendinger () with an up-to-date CV including any publication profile.

For questions related to your application, contact Lynne Barlow-Cheetham ()

(Link to Apply button -

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