Connectionists: PhD Position opening

[Eleni Vasilaki]

*PhD Position *

*The neuroeconomics of decision-making uncertainty:* brains, swarms and
markets network.

*University of Sheffield – Department of Computer Science *

*Supervisors:* Drs Eleni Vasilaki & Trevor Cohn, Machine Learning group,
Department of Computer Science. External supervisor: Prof. Jane Binner,
Chair of Finance, University of Birmingham.

The ML group was founded in 1998. The scientific focus of the group is
developing formalisms for data analysis, with a particular focus on
probabilistic modelling. The engineering focus of the group is on algorithm
development for modelling data in computational biology, language,
neuroscience and large unstructured data sources.

This project is part of a funded partnership with the departments of
Automatic Control and Psychology under the theme of Neuroeconomics. It
involves a close collaboration between Computer Science and Management
departments.

*Project description:*  Reinforcement learning and the equity premium
puzzle.

Humans often make decisions based on their desire to maximize profit or
reward. Such decisions take place within changing environments, where
optimal choices in the past may differ from those in the present. For
example, choosing a tracker-rate mortgage might have been at some time in
the past a better option than a fixed-rate but today this may have changed.
These choices are typically made under uncertain situations and involve a
degree of risk. Though the specifics of decision-making mechanisms are
still not fully understood, it is evident that fundamentally the human
brain is able to identify information sequences that could also correlate
with reward.

This project aims to develop a data driven framework for understanding
decision-making types of investors, and the key ingredients of making
successful investment decisions. We ask the question whether the choices of
successful investors have a higher component of sophisticated principles
versus the unsuccessful investors, and whether different mixtures of models
can account for different investor strategies. We anticipate that the
results would be of immediate interest to finance institutions that may
want to use our models to extract information about their clients' profiles
in order to provide customized financial training or making decisions about
investor loans.

*Candidate's profile:* The ideal candidate should have degree in Computer
Science, Mathematics, Physics, Engineering or similar, a very strong
mathematical background, excellent programming skills and interest in
financial problems. The PhD topic requires development and application of
Artificial Intelligence techniques for financial data analysis.

*Scholarship information:* The position covers tuition fees at UK/EU rate,
provides annual maintenance at the standard RCUK rate (£13,726 for
2013-14), and a contribution towards research and travel expenses of £1,000
p.a. Awards are open to UK, EU and international applicants. International
applicants will be required to prove that they have sufficient funds to
cover the difference between the UK/EU and Overseas tuition fees. For
exceptional international candidates there might be opportunities for
additional fee waivers.

Preliminary enquiries should be addressed to Dr Eleni Vasilaki or Dr Trevor
Cohn. Email: E.Vasilaki at sheffield.ac.uk or T.Cohn at sheffield.ac.uk*.*

*Application Procedure: Please submit an application for a PhD at the
department of Computer Science including a CV and a motivation letter.  For
more information and to apply:
http://www.sheffield.ac.uk/postgraduate/research/apply .*
 **
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