2 Fully-funded PhD Studentships in Computational Intelligence

ENDS: Explainability of Non-Deterministic Solvers

Robert Gordon University, University of Stirling,

British Telecom, ARR Craib & The Data Lab

 

Closing Date: 12 noon Friday 13th December 2019

 

Applications are sought for Research Studentships (PhD) in Computational Intelligence at Robert Gordon University (RGU) and at University of Stirling (UoS).

 

Proposed Research

This proposal is a cutting-edge investigation into explaining the decisions of commonly-used solvers for optimization problems. Two PhD students, one at RGU and one at UoS, will be working in partnership with BT and

ARR Craib. This will exploit existing expertise and collaboration between the academic and industrial partners, with the potential for a step-change advance in solving challenging industrial optimisation problems.

 

Explainable AI is a well-established concept, but research success in the area has mainly used methods that mimic human reasoning, making the path to solution readily understood by end-users. In non-deterministic solvers, the path to solution is driven by random processes that accumulate problem learning, as opposed to deduction from prior knowledge or experience. A description of these processes, while explanatory, is hard for non-experts to comprehend. The innovation in ENDS is to derive human-understandable knowledge about the problem from the non-deterministic solution process and translate that into an explanatory form for end-users.

 

Two distinct approaches, mining trajectories and surrogate models, will derive problem knowledge in a new way. Two approaches bring robustness to the project: both will produce interesting doctoral level research for the students, while decreasing the project risk. ENDS will further innovate by expressing the problem knowledge gained via natural language generation and visualisation. End-users with no understanding of the solvers will be able to assess presented solutions in the light of a comprehensible explanation. Finally we will innovate by applying ENDS to two real world domains: workforce management at BT and real time truck scheduling at ARR Craib. Multiple applications with multiple partners ensures wide applicability and industrial relevance.

 

Project Management

One student will be based at RGU under the supervision of Prof. McCall and the other at UoS under the supervision of Dr. Brownlee. Both students will spend some time periodically at the BT research facility at Adastral Park, Ipswich, working with the BT research team and applying their research to BT datasets. ARR Craib will provide operational data and end-user feedback to both students.

 

The studentships will be of 36 months duration, commencing in February 2019. The studentships are fully funded and include Home/EU tuition fees as well as a tax-free stipend of £15,000 per annum. Non-EU students may also apply but will be required to fund the difference between Home/EU fees and International fees.

 

Key Skills

Applicants should have a first class Honours degree or a Masters at Distinction level in Computing Science or a strongly related discipline. Strong programming skills are highly desirable. Some knowledge of non-deterministic optimization algorithms, in particular population-based techniques such as genetic algorithms, is also highly desirable, though not essential.  Applicants should have good personal and communication skills, strong professionalism and integrity and be confident working on their own initiative.

 

Applications

Applications should be emailed to CSDM_ResearchAdmin@rgu.ac.uk by 12 noon on Friday 13th December 2019. The applications should consist of a covering letter or personal statement of interest and a CV. Further information such as passport details or transcripts may be requested during the short-listing stage. Interviews will take place at RGU in Aberdeen in the week commencing 6th January 2020.

 

All enquiries should be addressed jointly to:

 

Prof. John McCall, j.mccall@rgu.ac.uk and

Dr. Alexander Brownlee, alexander.brownlee@stir.ac.uk