Post-doctoral position in statistics/machine learning, University of Helsinki

The International Society for Bayesian Analysis; Helsinki; On-site; EUR 40,000 - 55,000; Full time

Job summary

A leading research university in Helsinki is offering a post-doctoral position in statistics and machine learning for a 3+1 year project. The role involves developing hierarchical Bayesian models aimed at analyzing ecological and environmental data. Ideal candidates have experience with Bayesian methods and scientific computing, although prior knowledge in ecology is an advantage. This position offers a collaborative environment and flexibility based on the candidate's skills.

Qualifications

• Experience in developing Bayesian methods for computational challenges.
• Familiarity with multivariate models is advantageous.
• Experience in scientific computing is required.

Responsibilities

• Develop Bayesian models for ecological data analysis.
• Collaborate with project team on model applications.
• Tailor model direction based on candidate's interests.

Skills

Bayesian methods

Scientific computing

Multivariate generalized linear mixed models

Factor models

Gaussian processes

Education

PhD in statistics or related field

Job description

Post-doctoral position in statistics/machine learning, University of Helsinki

Aug 17, 2018

Department of Mathematics and statistics at University of Helsinki has an open 3+1 year post-doctoral position in a project funded by Academy of Finland. The objective of the project is to develop novel hierarchical Bayesian models and computational methods for analyzing long-term spatially structured ecological and environmental data sets.

Position description and application instructions can be found here:
https://www.helsinki.fi/en/open-positions/postdoctoral-researcher-statistics-machine-learning

A successful candidate will have experience in the development and application of Bayesian methods for computationally challenging problems. In particular, prior experience in multivariate generalized linear mixed models, factor models and/or Gaussian processes is highly valued. A successful candidate will also have experience in scientific computing. Prior experience in ecology is not necessary, but is counted as an advantage. The exact direction to which the post doc will develop the models can be agreed upon based on the experience and interests of the candidate.

For more information, contact assistant Professor Jarno Vanhatalo by email: [e-mail].

Yours,

—
Jarno Vanhatalo,
D.Sc. (Tech.), Assistant Professor of statistics
Department of Mathematics and Statistics and
Organismal and Evolutionary Biology Research Programme
University of Helsinki