Two Postdoctoral Researchers in statistics for large-scale environmental applications

Updated: 3 months ago
Deadline: 05 Mar 2023

The University of Helsinki , founded in 1640, is one of the world’s leading universities for multidisciplinary research. The university has an international academic community of 40,000 students and staff members. The University of Helsinki offers comprehensive services to its employees, including occupational health care and health insurance, sports facilities, and opportunities for professional development. The International Staff Services office assists employees from abroad with their transition to work and life in Finland.

As part of the Faculty of Science, the Department of Mathematics and Statistics of the University of Helsinki is the biggest university department of mathematical sciences in Finland. The department hosts several current ERC grants of the European Research Council and two Centres of Excellence of the Academy of Finland.

The Department of Mathematics and Statistics at the Faculty of Science of the University of Helsinki invites applications for


in Bayesian statistics and its applications to ecological data and biodiversity monitoring for a fixed term of 2-3 years, starting on 1 April or as agreed. There will be a trial period of six months in the beginning.

The successful applicant should have doctoral degree in statistics, machine learning, or other relevant field, and have experience in developing and applying Bayesian methods in computationally challenging problems. Prior experience in ecology is not necessary, but considered an advantage. We seek candidates with the ability to efficiently execute and complete research projects. Overall, excellent written and verbal communication skills are required for extensive collaborations. The exact direction of the work will be agreed upon based on the experience and interests of the candidate.

The postdoctoral researchers will work in the Environmental and Ecological Statistics Group , led by Associate Professor Jarno Vanhatalo, and contribute to two international and cross-disciplinary research projects of the group:

  • Developing future strategies for the monitoring of biodiversity and for sustained and cost-efficient collection of long-term biodiversity data. The core of the work will be developing methods, algorithms and statistical theory utilizing ideas from Bayesian model-based experimental design and value of information analyses, considering both static and adaptive designs. This work will be done in collaboration with several other post-doctoral researchers and senior scientists with backgrounds in statistics, mathematics and ecology. In this project, we seeks to make ground-breaking impact to our ability to track and manage changes in biodiversity. 
  • Developing future tools for analyzing and mapping large scale data of marine biota through development of novel multivariate statistical and computational methods. Our approach is based on hierarchical Bayesian models and methods that allow us to integrate heterogeneous, but complementary, ecological and environmental data. The work focuses specifically on the so called joint species distribution modeling (JSDM) framework, which are multivariate models and among the most important statistical tools in community ecology today. In this project, we will tailor such models for applications to ecological data from across European marine areas, utilizing novel latent factor and Gaussian process models. A successful candidate will participate also in developing novel predictive model comparison and assessment methods to compare and validate our models. You will work in collaboration with several other post-doctoral researcher and senior scientists from across the Europe in a project that seeks to make ground-breaking impact to our ability to sustainably manage our marine biodiversity.
  • Both project involve utilizing existing extensive long-term ecological datasets to assess and test the ideas, as well as communicating and developing practical proposals to the empiricists and managers utilizing the methods and other end-products in the future.

    For our recent methodological publications relevant for these projects, see the reference list at the end.


    We offer you highly international, interdisciplinary, and distinguished research environment with excellent opportunities to build up your professional expertise and scientific network. Both of the projects also offer excellent possibilities to make research with a societal impact in relation to sustainable development. The environmental and Ecological Statistics group is affiliated both in the Department of Mathematics and Statistics (Faculty of Science) and in the Research Center for Ecological Change (REC; Faculty of Bio- and Environmental Sciences).  The REC is one of the largest research communities in ecological research in Finland, which hosts some 60 researchers, and provides excellent opportunity to to develop models in collaboration with the empiricists with system knowledge.

    The salary of the successful candidate will be based on level 5 of the demands level chart for teaching and research personnel in the salary system of Finnish universities. In addition, the appointee will be paid a salary component based on personal performance. The starting salary will be ca. 3500–3800 euros/month, depending on the appointee’s qualifications and experience.

    The University of Helsinki offers comprehensive services to its employees, including occupational health care and health insurance, sports facilities, and opportunities for professional development. The University provides support for internationally recruited employees with their transition to work and life in Finland. For more on the University of Helsinki as an employer, please see .

    The University of Helsinki seeks to promote an equitable and inclusive working environment and welcomes applicants of any gender, linguistic and cultural background, or minority group.


    The application should include the following documents as a single pdf file:

    • motivational letter telling which of the positions you are interested in and outlining why you are the right person for this task (max 1 page)
    • CV (max 2 pages)
    • list of publications

    Include also contact information of two persons, who are willing to provide a reference letter by separate request.

    Please submit your application inthe University of Helsinki´s recruitment system. Applicants, who are employees of the University of Helsinki, are requested to leave their application by using the employee login.

    The deadline for submitting the application is 5 March 2023.


    For more information on the position and operational environment, contact by email: Jarno Vanhatalo (jarno.vanhatalo(at)

    If you need support with the recruitment system, please contact HR Specialist Jussi Hartikainen (jussi.a.hartikainen(at)

    Some recent relevant publications
    Vanhatalo, J., Hartmann, M. and Veneranta, L. (2020). Additive multivariate Gaussian processes for joint species distribution modeling with heterogeneous data. Bayesian Analysis, 15(2):415–447.

    Vanhatalo, J., Foster, S. D. and Hosack, G. R. (2021). Spatiotemporal Clustering using Gaussian Processes Embedded in a Mixture Model. Environmetrics, 32:e2681

    Liu, J. and Vanhatalo, J. (2020). Bayesian model based spatio-temporal sampling designs and partially observed log Gaussian Cox process. Spatial Statistics, 35:100392

    Foster, Vanhatalo, Trenkel, Schulz, Lawrence, Przeslawski, and Hosack (2021). Effects of Ignoring Survey Design Information for Data Reuse. Ecological Applications,31(6):e02360

    Itter, M.  Kaarlejärvi, E., Laine, A.-L., Hamberg, L., Tonteri, T. & Vanhatalo, J. 2022. Identifying and separating the processes underlying boreal forest understory community assembly. Preprint available at bioRxiv. Doi: 10.1101/2022.05.03.490480

    Kettunen, J., Mehtätalo, L., Tuittila, E-S., Korrensalo, A. and Vanhatalo, J. (2021). Joint Species Distribution Modeling with species competition and non-stationary spatial random effects. arXiv:2111.02460

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