[APDIO] The DIAMOND Initiative: Eight fully funded PhD studentships in Operational Research, Data Science, and Mathematical Modeling.

[Joerg Fliege <J.Fliege@soton.ac.uk>]

The DIAMOND Initiative: Eight fully funded PhD studentships in
Operational Research, Data Science, and Mathematical Modeling.

The Southampton initiative “from Data and Intelligence via ModelliNg
to Decisions” (DIAMOND) invites applications for their fully funded
PhD studentships. DIAMOND is run jointly by Mathematical Sciences of
the University of Southampton and the Southampton Business School.  It
offers graduate students an intensive training and research programme
that equips them with the skills needed to tackle modern problems in
Operational Research, Data Science, and mathematical modeling.

This year, DIAMOND offers eight fully funded studentships to strongly
motivated students.  Scholarships will be awarded on a competitive
basis. Applicants should have or expect to obtain the equivalent of a
UK first class or upper second class honours degree (and preferably a
master’s degree) in mathematics, computer science, engineering or
other relevant discipline. The studentship provides a maintenance
grant at the Research Council UK rate and tuition fees at the UK/EU
rate. Applications should include a cover letter, CV, detailed
academic transcripts and the contact details for at least two academic
referees.

We presently offer the following projects, all sponsored by our
industrial partners:

•       Analytics for condition based maintenance. Sponsored by
Siemens UK,  this project will focus on developing a combination of
data- and model-driven approaches to support the condition based
maintenance of rolling stock equipment.
•       Exploiting symmetries for network control. Network control
involves the identification of an optimal set of driver nodes, such
that the given system can be steered in a desired direction.
Identifying such nodes poses an extremely difficult optimization
problem in practical applications. In this project we will develop
algorithms and techniques to solve network control problems exploiting
recent results on network symmetries.
•       Global supply chain and resource management for aircraft
fleets in uncertain environments. We will develop novel mathematical
models and optimisation algorithms for joint supply chain and resource
management in an uncertain environment. Working closely with the
project sponsor Boeing Defense UK, you will create mathematical tools
that support decision makers in maintaining flexibility and robustness
in resource management to meet an increasing variety of expectations
in a dynamic environment.
•       Maximising the throughput of production and assembly lines
using symbiotic simulation optimisation.  Working with Ford motor
company’s Powertrain Manufacturing Engineering department, this
project will realise the full benefits of Industry 4.0 on increasing
manufacturing throughput through developing novel optimization via
simulation techniques that work with symbiotic simulations.
•       Optimal Control of Autonomous Vehicles in Highly Dynamic
Environments. Sponsored by Northrop Grumman, you will consider
mathematical models and computational tools for robust control of
swarms of autonomous vehicles in highly dynamic environments.
•       Predictive maintenance through advanced data exploitation.
Based on advanced machine learning concepts, we want to develop
predictive maintenance tools for fleets of vehicles. The industrial
project sponsor provides a data analytics capability for an advanced
Health and Usage Monitoring System whereby sensors record hundreds of
parameters per second and thousands of fault codes and user
notifications.
•       Random forests for noisy applications in finance. In the
financial and insurance sector, machine learning ML challenges usually
arise either in a setting where data is contaminated by noise or the
model to be learned is of a stochastic nature.  In close collaboration
with our partner DEVnet, we will investigate selected variants of
regression trees.
•       Reliability Analytics: This project will focus on developing
approaches to reliability modelling of key Rolling Stock components
based on Siemens Maintenance Management System and Train Diagnostic
data.

Students will be part of the vibrant research environment of CORMSIS,
the Centre for Operational Research, Management Science, and
Information Systems. CORMSIS at the University of Southampton has an
established breadth and depth in Operational Research unrivalled in
the UK. Our research centre applies advanced mathematical and
analytical modelling to help people and organisations make better
decisions. CORMSIS is the largest Operational Research group in the
UK, spanning Mathematical Sciences and Southampton Business School.
Among the many areas of expertise, it has extensive breadth and depth
of experience in mathematical modelling and optimisation, but covers
the whole spectrum of current OR/MS/IS from mathematical optimisation
through business analytics and simulation to qualitative research in
problem structuring.. In the QS World Rankings by Subject 2019,
Operational Research and Statistics at the University of Southampton
are placed at 48th in the world and 7th in the UK.
(http://www.southampton.ac.uk/cormsis/)

Application deadlines:

Maximising the throughput of production and assembly lines using
symbiotic simulation optimisation: 30 June 2019.
All other projects: 15 July 2019.

How to apply:

Please have a look at
https://www.southampton.ac.uk/maths/postgraduate/research_degrees/apply.page

For informal discussions please contact Professor Joerg Fliege,
J.Fliege@soton.ac.uk