22 October: fyi -- phd funding, Cork
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POST-GRADUATE POSITIONS IN RISK MANAGEMENT AT UNIVERSITY COLLEGE CORK
These PhD positions are jointly funded by the Irish Research Council for
Science and Engineering Technology (IRCSET) and IBM. Their overarching
objective is to bring together complementary approaches to risk assessment
and delivery. Three different strands will work alongside IBM in carrying
out new research to make users better informed about risk through a wide
variety of mathematical and AI instruments.
Please apply to Dr James Little (jlittle@4c.ucc.ie) with a current CV
stating which of the three strands you wish to apply for.
1. Stochastic Constraint Programming for Minimising Risk
Stochastic Programming is a mature subfield of Operations Research that adds
probabilistic reasoning to Integer Linear Programming. Constraint
Programming is a younger subfield of Artificial Intelligence aimed at
deterministic (non-stochastic) problems, with a rich modelling language and
a large family of powerful solution algorithms. Stochastic Constraint
Programming (SCP) is a recent hybrid of the two frameworks, designed to
compactly model and efficiently solve problems involving both constraints
and uncertainty. However, SCP requires further development in order to be a
useful real-world tool: several risk metrics are not modelled and most
current solution algorithms do not scale up to large applications.
This project will extend the SCP framework to include such risk metrics, and
develop some recent algorithms that promise to be much more scalable.
Principal Investigator: Dr Steve Prestwich, Cork Constraint Computation
Centre, Dept of Computer Science, University College Cork
The candidate should have (or be about to obtain) a good honours degree in
Computer Science, Engineering or related subject with competent programming
skills.
2. Imprecise Probabilities for Reasoning with Risk
There is a substantial literature on probabilistic approaches for risk
analysis. However, it can be very hard to obtain reliable and precise values
of the probability of some events, especially if we do not have good
historical data about such events, or if changed circumstances reduce the
relevance of previous data or experience. The cost or value of an event can
be even harder to reliably estimate. Furthermore, an expert can sometimes be
more comfortable with giving more qualitative probabilistic or cost
information. A number of theories of reasoning with uncertainty have been
developed - in particular, in the Artificial Intelligence community - which
are related to notions of imprecise probability. The aim of this project is
to develop models of risk analysis based on imprecise probabilities and
other uncertainty and cost formalisms, in particular, for business process
models.
Principal Investigator: Dr Nic Wilson, Cork Constraint Computation Centre,
Dept of Computer Science, University College Cork
The candidate should have (or be about to obtain) a good honours degree in
Computer Science, Mathematics, Engineering, Economics, Business Studies or a
related subject, strong mathematical ability and competent programming
skills.
3. New Methods for Communicating about Risk
Decades of research have revealed many cognitive biases that systematically
distort human understanding of risk. More recently, organisations have begun
to use this research to build corrective procedures into processes involving
human judgements about risk. The aim of this project is to build on both of
these developments and create new software tools for communicating
judgements about risk. These tools will be tested against current methods
to provide robust evidence that they improve the accuracy and
intelligibility of communication processes in which people attempt to convey
to others their own perceptions and evaluations of risk and uncertainty.
Principal Investigator: Dr Dylan Evans, School of Medicine, University
College Cork
The candidate should have (or be about to obtain) a good honours degree in
Computer Science, Psychology, Graphic Design or a related subject, and have
competent programming skills.
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