Monday, October 8, 2018

[DMANET] PhD studentship in optimization, Strathclyde Business School (Glasgow, UK)

A PhD studentship in optimization is available at the Dept. of Management Science, University of Strathclyde Business School, Glasgow, UK, for the project entitled "Multi-Level Robust Optimization: Theory, Algorithms and Practice". Funding is available to cover 3 years of tuition fees plus a tax free stipend (£14,777 per year for 2018-19, and increased every academic year). The studentship is open to candidates from any nationality (UK/EU/international), and the fees will be fully covered at the international level.

The application deadline is midnight of 25th October 2018 Thursday. There may be a short phone/Skype interview on 29th and 30th October, so the applicants should be available on either of these two days. The successful candidate will be expected to start their PhD in January 2019 (but no later than 1st April 2019 due to funding constraints). Only complete applications (including a successful English test result, if applicable) from excellent candidates will be accepted, please see eligibility below before applying.

Funding is available to cover 3 years of tuition fees at the UK/EU/international level, plus a tax free stipend of £14,777 for 2018-19, increased incrementally every year for inflation. The student will also have access to a minimum travel fund of £3,500 (with further funds from the department available) to attend a conference and/or specialized training (such as NATCOR courses) every year.

The PhD project requires a highly numerate graduate with interests in mathematical optimization. Candidates should have at least a First Class Honours degree or equivalent (e.g., a B.Sc. degree with 3.4 GPA in a 4.0 system), and/or preferably a Master's degree in a quantitative discipline such as industrial engineering, operations research, mathematics or computer science (amongst others). Experience in programming and fundamental knowledge in optimization are not essential but highly desirable (there will be training opportunities throughout PhD). Candidates who are not native English speakers will be required to provide evidence for their English skills (such as by IELTS or similar tests that are approved by UKVI, or a degree completed in an English speaking country - please note that this is essential for visa applications of non-EU candidates. If you have a scheduled test before 1st November 2018, also feel free to submit your application).

Applications are admitted until end of Thursday 25th October 2018.

Please submit your complete applications to Dr Euan Barlow (, with email subject "PhD application for multi-level robust optimization". Your application should include:
- One-page cover letter,
- CV,
- Scans of any university degree certificates and transcripts (no other certificates),
- English test results (if applicable - check UKVI webpages for further information), and
- Two recommendation letters (or contact details of two referees, if letters are not available to them).

There may be brief (20 minutes) phone/Skype interviews on Monday 29th October and Tuesday 30th October, so if you are available only on one of these days, please ensure to note this in your cover letter. You will be notified end of Friday 26th October if you are shortlisted for an interview.

If you are a non-EU/EEA citizen requiring a visa to study in the UK, please ensure to check information available on official UKVI websites, in particular for recognized English tests (note TOEFL is *not* recognized) and recognized English speaking countries, if you are a national of or obtained a higher education degree in one of these countries.

Applications not satisfying the eligibility requirements will be disregarded.

More information about the department can be found here:

Informal enquiries about the project should be directed to Dr Euan Barlow (

In practice, decision-problems which concern planning horizons spanning several months or years will typically involve various sources of uncertainty, and in many cases these uncertainties will impact decisions over differing time-scales and in different levels of the problem. Accurately accounting for these uncertainties in an optimization model is challenging with traditional approaches, as all uncertainties would generally be treated simultaneously. The proposed project will develop novel optimization theory and methodologies to tackle this general class of problems, namely multi-level robust optimization problems (and potentially its extension to multi-objective problems). Efficient algorithmic approaches to tackling this new class of optimization problem will then be investigated.

As various complex logistics problems demonstrate, many real world problems come in the form of multi-level decision problems with different uncertainties apparent at different levels of the problem. Such complex decision-problems necessitate computationally tractable frameworks which can enable key stakeholders to establish near-optimal decisions in short time-scales, in addition to establishing a rigorous theoretical understanding of the mathematical properties of these problems in order to design and develop computational tools most effectively. Although some efficient approaches exist for the multi-level optimization problems, there remain challenges in solving these problems, e.g., how do different levels interact with each other and how much ''influence'' do they have on each other. Such challenges increase in the presence of uncertainties, and are magnified even further when the problems also become multi-objective, as the analysis on how to weigh different objectives is extremely complex. There is only very limited and application-specific work in the literature regarding parameter uncertainties in case of multi-level optimization problems, albeit without any theoretical understanding of the underlying mathematical structures and challenges.

The PhD project aims to address research questions such as:
- Can we establish a thorough theoretical understanding of the structure of the robust multi-level optimization problems, including alternative reformulations of the problems and their theoretical strengths and weaknesses? Furthermore, can we extend such results to the case of multi-objective problems?
- Can we exploit the statistical or structural properties of these problems in the design of efficient algorithms? How effective can such algorithms be, whether in regard to theoretical or computational limitations?
- Can we apply these algorithms effectively to case studies representing a number of different practical decision-problems?

Depending on the interests and skills of the successful PhD student, there is flexibility on which research question(s) to pursue.

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