A 3.5 year EPSRC-funded studentship in Statistics at the University of Kent is available from September 2012 to work on advanced Bayesian computational methods for modelling high-frequency financial data.
The student will be part of a interdisciplinary research project to develop advanced statistical theory and algorithms that will directly address current challenges in scientific modeling in econometrics, biology and astronomy. This research is a collaboration with experts in statistical machine learning (Prof. Zoubin Ghahramani, Cambridge), systems biology (Prof. David Wild, Warwick) and astronomy (Prof. Andrew Liddle, Sussex).
The project will involve developing fast computational method for inference in high frequency financial data using recently developed Bayesian nonparametric models. It will combine ideas from machine learning, statistics and financial econometrics.
The statistics division at the University of Kent is based in the School of Mathematics, Statistics and Actuarial Science. It offers a thriving research environment and has been rated within the top ten statistics groups in the country in successive national research assessments.
Candidates should have a good first degree in a relevant quantitative field such as mathematics, statistics, theoretical physics, econometrics or computer science and a strong interest in modelling financial data. Good programming skills in a high level language such as Matlab, R or C/C++ would be advantageous.
For informal discussions, and applications, please contact Dr. Jim Griffin ([log in to unmask]) in the first instance.
Applicants should include a full CV and accompanying letter outlining their interests and any previous work.
The deadline for applications is 9th March.
You may leave the list at any time by sending the command
SIGNOFF allstat
to [log in to unmask], leaving the subject line blank.
|