Decision Analytics & Risk
Closing Date:Sunday 31 May 2020
EPSRC Studentship – Decision Analytics & Management Science
Southampton Business School
Location: Highfield Campus
Salary: £15,141 (20/21 UKRI rate)
Closing Date: 4:00pm, 31 May 2020
A fully funded EPSRC studentship is available for October 2020 entry within the Management Science group in the Department of Decision Analytics and Risk of Southampton Business School. Three potential projects and supervisory teams are outlined below but many other topics are possible, across the wide range of staff expertise within the Management Science group. There is only one EPSRC studentship, and it will be awarded solely on the basis of the academic quality of the candidate. However, strong applications will be considered for funding from alternative sources.
Applicants seeking further information about one of the three topics below should contact the supervisor indicated. Applicants interested in other topic areas should contact Professor Sally Brailsford (email@example.com) in the first instance, giving a brief description of the area of interest. This must fit within the EPSRC remit of Operational Research and Management Science.
Topic 1: Risk Assessing Financial Technology (FinTech) for Financial Inclusion. The project will focus on examining the opportunities for developing an Artificial Intelligence (AI) driven framework in risk assessing and scoring small to medium sized enterprises (SMEs) in developing economies, such as Ghana. We are specifically interested in applicants with an appreciation of modern financial technologies and how these may improve underserved businesses in emerging markets. Supervisors: Nic Dacre (Nicholas.Dacre@soton.ac.uk), Simonov Kusi-Sarpong.
Topic 2: Smart Price and Assortment Decision Making Using Deep Reinforcement Learning. This project aims to develop new multi-period optimisation models for smart price and assortment decision making. Recent state-of-the-art models and algorithms, such as multi-armed bandits, dynamic programming, and reinforcement learning with approximation (e.g., deep reinforcement learning, Q-learning) will be used in solving the dilemma between information gathering and reward seeking. Supervisors: Christophe Mues (C.Mues@soton.ac.uk), Huan Yu.
Topic 3: Greening economic usage scenarios of ships: data structures to support ex-ante performance assessment. The next decade will see tremendous development in new ship designs, supporting technology, and harbour infrastructure in the quest towards sustainable practises. A myriad of small, but sometimes drastic or disruptive changes will arise that will transform the industry. These may include retrofitting solutions, operational adjustments, cleaner energy production, and artificial intelligence support. This project will focus on data mining and data analytics techniques for capturing appropriate data as input to ex-ante performance assessment, to help decision makers evaluatenew combinations of potential ideas. Supervisors: Patrick Beullens (P.Beullens@soton.ac.uk), Dominic Hudson.
Full tuition fees for EU/UK students plus for UK students, an enhanced stipend of £15,141 tax-free per annum for up to 3.5 years (pro-rata for part-time students).
A good BSc (first or upper second, or international equivalent) in Mathematics, Statistics or a related quantitative subject, ideally with an MSc in Operational Research, Management Science, Business Analytics, Computer Science or related subject. All these topics will require analytical, optimisation and software programming skills and a passion for academic research and problem solving.
The closing date and time for applications is 4pm on 31 May 2020. All applications must be made online. For further information about the PhD programme in the Business School, and a link to the online application page, see https://www.southampton.ac.uk/business-school/postgraduate/research_degrees/courses/phd.page . Please indicate in the funding section of the on-line application form, that you wish to be considered for this funded studentship. Please also email firstname.lastname@example.org to inform the Graduate School Team that you are applying.
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