Research Fellow (Statistician, Machine Learning Specialist)
£29,704 - £35,469
A research post is available immediately at the University of Edinburgh for a Bayesian statistician/computer scientist to work on developing statistical methods for genetic epidemiology. The object is to develop methods for modelling the joint effects of genotype and environment and inferring causal relationships, using tools such as Markov chain Monte Carlo simulation, variational Bayes approximations, and Dirichlet process models. The successful applicant is likely to learn about and make use of the recently released Infer.NET (http://research.microsoft.com/infernet), as well as developing other techniques.
The post would suit an individual at post-doctoral level with experience of using and developing Bayesian probabilistic methods for modelling and inference. Previous experience in statistical genetics or bioinformatics is useful, but is far from an essential requirement: experience in novel
development and application of probabilistic machine learning and statistical methods in other fields could be just as valuable. The post is funded by the Medical Research Council for three years with possibility of extension should further funding be obtained.
Closing date: 9 January 2009.
See http://www.jobs.ac.uk/jobs/RO886 /Research_Fellow_Statistician_Machine_Learning_Specialist/
Interested applicants can contact Paul McKeigue (paul.mckeigue@ed.ac.uk) or Amos Storkey (a.storkey@ed.ac.uk) for further details.
£29,704 - £35,469
A research post is available immediately at the University of Edinburgh for a Bayesian statistician/computer scientist to work on developing statistical methods for genetic epidemiology. The object is to develop methods for modelling the joint effects of genotype and environment and inferring causal relationships, using tools such as Markov chain Monte Carlo simulation, variational Bayes approximations, and Dirichlet process models. The successful applicant is likely to learn about and make use of the recently released Infer.NET (http://research.microsoft.com/infernet), as well as developing other techniques.
The post would suit an individual at post-doctoral level with experience of using and developing Bayesian probabilistic methods for modelling and inference. Previous experience in statistical genetics or bioinformatics is useful, but is far from an essential requirement: experience in novel
development and application of probabilistic machine learning and statistical methods in other fields could be just as valuable. The post is funded by the Medical Research Council for three years with possibility of extension should further funding be obtained.
Closing date: 9 January 2009.
See http://www.jobs.ac.uk/jobs/RO886 /Research_Fellow_Statistician_Machine_Learning_Specialist/
Interested applicants can contact Paul McKeigue (paul.mckeigue@ed.ac.uk) or Amos Storkey (a.storkey@ed.ac.uk) for further details.
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