The Department of Biosciences at the University of Helsinki invites applications for a
2 POST DOC POSTITIONS IN STATISTICAL ECOLOGY
for a fixed term of three years. There will be a trial period of four months in the beginning. The post doc positions are part of the Research Centre for Ecological Change and are funded by the Jane and Aatos Erkko Foundation for 1.1.2018-31.12.2020. PIs of the Centre are prof. Anna-Liisa Laine, prof. Otso Ovaskainen, prof. Tomas Roslin, assist. prof. Jarno Vanhatalo and dr Marjo Saastamoinen. The starting date is 1.1.2018, but a later starting date can be negotiated.
The overreaching aim of the Centre is to generate a coordinated analysis of long-term ecological data to understand impacts of global change. To unravel how populations and interactions between species in nature are responding to ongoing environmental change, the project takes advantage of the unique long-term datasets collected in Finland. The centre also develops state-of-the-art methodology for analysing long-term spatially structured data sets within a joint species distribution modeling framework. For more information on the Centre, please visit https://www.helsinki.fi/en/researchgroups/research-centre-for-ecological-change
The salary of the successful candidate will be based on level 5 – 6 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. 3300 – 3800 euros/month, depending on the appointee’s qualifications and experience.
The deadline for submitting the application is 7 October 2017.
A link to the University of Helsinki Recruitment System where applications can be submitted will be available after mid September at:
The post docs take part in the development of statistical methods for analyzing long-term ecological data and in statistical analyzes within the Research Centre for Ecological Change.
The methodological work focus on development of Hierarchical Modelling of Species Communities (HMSC) and hierarchical multivariate Gaussian processes. HMSC is a joint species distribution modelling framework that can be used for the statistical analysis of data on species occurrence, environmental covariates, functional traits and phylogenetic relationships. HMSC can be applied to hierarchical, spatial and temporal study designs, and it applies to many kinds of response data (presence/absence, counts, etc.). Gaussian processes are flexible and versatile modeling approach that are emerging to statistical ecology as tools for species distribution and population dynamics modeling. Gaussian processes are used to model spatial and spatiotemporal heterogeneity in data and describe species responses to their environment in nonparametric manner.
For recent methodological publications, see the reference list at the end. For the R and MATLAB implementations of HMSC, see the project’s www-page https://www.helsinki.fi/en/researchgroups/metapopulation-research-centre/hmsc
A successful post doc 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 HMSC and Gaussian process models can be agreed upon based on the experience and interests of the candidate.
For more information, contact prof. Otso Ovaskainen and/or assistant prof. Jarno Vanhatalo by email: email@example.com, firstname.lastname@example.org
Ovaskainen, O., Tikhonov, G., Norberg, A., Blanchet, F. G., Duan, L., Dunson, D., Roslin, T. and Abrego, N. 2017a. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecology Letters 20, 561-576
Ovaskainen, O., Tikhonov, G., Dunson, D., Grøtan, V., Engen, S., Sæther, B.-E. and Abrego, N. 2017b. How are species interactions structured in species rich communities? A new method for analysing time-series data. Proceedings of the Royal Society B: Biological Sciences 284, 20170768.
Vanhatalo, J., Hosack, G. R. and Sweatman, H. (2017). Spatio-temporal modelling of crown-of-thorns starfish outbreaks on the Great Barrier Reef to inform control strategies. Journal of Applied Ecology, 54:188-197.
Hartmann, M., Hosack, G. R., Hillary, R. M. and Vanhatalo, J. (2017). Gaussian process framework for temporal dependence and discrepancy functions in Ricker-type population growth models. Annals of Applied Statistics, in press: http://imstat.org/aoas/next_issue.html