POSTDOCTORAL RESEARCHER in statistics and machine learning

The International Society for Bayesian Analysis; Helsinki; On-site; EUR 35 000 - 45 000; Full time

Job summary

A leading organization is seeking a POSTDOCTORAL RESEARCHER in statistics and machine learning for a two-year fixed term position. The role focuses on developing cutting-edge statistical methods for analyzing complex ecological data using Bayesian hierarchical models within a collaborative research environment.

Qualifications

• PhD required, preferably in statistics or a related field.
• Experience with Bayesian methods and statistical modeling is essential.
• Strong skills in programming for data analysis are desirable.

Responsibilities

• Develop statistical and computational methods for analyzing large datasets.
• Focus on Bayesian hierarchical models and predictive model comparison.
• Work within the framework of joint species distribution modeling (JSDM).

Skills

Bayesian hierarchical models

Multivariate statistics

Computational methods

Predictive modeling

Education

PhD in Statistics or related field

Job description

POSTDOCTORAL RESEARCHER in statistics and machine learning

Feb 4, 2020

Dear all

We are looking for a POSTDOCTORAL RESEARCHER in statistics and machine learning for a fixed term of two years.

The deadline for applications is March 1st.

The post doc position is part of the Research Centre for Ecological Change (REC) and will be placed at the Department of Mathematics and Statistics and the Organismal and Evolutionary Biology Research Programme.

The position is aimed at developing statistical and computational methods for analyzing large and heterogeneous data. The methodological work focuses specifically on development of Bayesian hierarchical multivariate spatio-temporal models and predictive model comparison methods within so-called joint species distribution modeling (JSDM) framework. JSDMs are multivariate models that can be applied to hierarchical, spatial and temporal study designs, and many kinds of response data. The JSDMs used in this project are built around novel latent factor and Gaussian process models.

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