Postdoctoral Researcher in Bayesian statistics for multivariate spatial and temporal data

The International Society for Bayesian Analysis; Helsinki; On-site; EUR 35,000 - 50,000; Full time

Job summary

A research institution is seeking a Postdoctoral Researcher in Bayesian statistics to develop innovative statistical methods for ecological data. The successful candidate will engage in cross-disciplinary projects and work with long-term datasets, contributing to advancements in community ecology. Candidates with experience in Bayesian statistics and multivariate analysis are encouraged to apply. The position is for 2-3 years, starting on 1 October, with applications due by 17 September 2023.

Qualifications

• Experience with Bayesian statistics and multivariate analysis.
• Ability to develop computational methods.
• Familiarity with community ecology is an advantage.

Responsibilities

• Develop hierarchical Bayesian models for ecological data.
• Contribute to cross-disciplinary research projects.
• Utilize long-term datasets for model validation.

Job description

Postdoctoral Researcher in Bayesian statistics for multivariate spatial and temporal data

Aug 24, 2023

A two post-doctoral researcher position in Bayesian statistics for multivariate spatial and temporal data for a fixed term of 2-3 years, starting on 1 October or as agreed in University of Helsinki:

The postdoctoral researchers will work in the Environmental and Ecological Statistics Group, led by Associate professor Jarno Vanhatalo, and contribute to the cross-disciplinary research projects of the group through development of novel multivariate statistical and computational methods for large scale ecological data. Our approach is based on hierarchical Bayesian models and methods that allow us to integrate heterogeneous, complementary, ecological and environmental data. In this project, the work focuses specifically on the so called joint species distribution modeling (JSDM) framework, which are multivariate predictive tools utilizing novel latent factor and Gaussian process models. JSDMs are among the most important statistical tools in community ecology today. One of your tasks will be to develop these models, and related computational inference algorithms, for applications to heterogeneous large scale ecological data. You will participate also in developing novel predictive model comparison and assessment methods to compare and validate the model predictions.

In addition to methodological work, the project involves utilizing existing extensive long-term ecological datasets to assess and test the ideas. In this, you will collaborate with ecologists utilizing the methods and analysis end-products. Prior experience in ecology is not necessary, though, but considered an advantage.

The deadline for submitting the application is 17 September 2023.