PhD Studentship in Statistical Machine Learning and Computational Systems Biology (Helsinki, Finland)

PhD studentship in developing novel probabilistic modelling and statistical inference methodology and applying these methods to problems in computational systems biology

Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki


The Helsinki Institute for Information Technology (HIIT) and the Department of Computer Science at the University of Helsinki are looking for a skilled

The Department of Computer Science is the leading unit for computer science research and education in Finland. The focus areas of research and teaching at the department are (1) algorithms and machine learning, (2) networking and services, and (3) software systems. Three Finnish Academy-funded centres of excellence operate at the department, and it works in close collaboration with the Helsinki Institute of Information Technology. The department is one of ten national centres of excellence in university education. The department employs some 170 persons, and its total budget is 11 Million Euros. The department has an outstanding research infrastructure, including a 1920-core computing cluster

The doctoral student will develop novel probabilistic modelling and statistical inference methodology incorporating structured prior information from mechanistic models and apply these methods to problems in computational systems biology. The aim of the project is to develop hierarchical Gaussian process models for modelling gene expression and regulation in complex experiments, such as with evolutionarily related specimen. The work will take place in the group of Dr Antti Honkela but it will involve collaboration with experimental biologists. The project will build upon recent experience in application of Gaussian process models on modelling gene regulation by Dr Honkela and collaborators (Honkela et al., PNAS 2010; Titsias et al., BMC Systems Biology 2012).

A successful applicant must have a MSc degree in computer science, electrical engineering, mathematics, physics, or a related field. A strong mathematical background and an interest in Bayesian modelling and/or machine learning are necessary. An interest in computational biology is essential but no prior experience is necessary.

The application deadline is 21 June 2012.
For more details and application instructions, see