The University of Helsinki (https://www.helsinki.fi/en) is an international scientific community of 40,000 students and researchers. It is one of the leading multidisciplinary research universities in Europe and ranks among the top 100 international universities in the world. It offers comprehensive services to its employees, including occupational health care and health insurance, sports facilities, and opportunities for professional development. The International Staff Services (https://www.helsinki.fi/en/university/working-at-the-university) office assists employees from abroad with their transition to work and life in Finland.

The Department of Mathematics and Statistics is the largest university department for mathematical sciences in Finland. The multifaceted research carried out at the Department of Mathematics and Statistics has received the highest points in several evaluations. The Organismal and Evolutionary Biology Research Programme is situated at the Viikki science park and belongs to the Faculty of Biological and Environmental Sciences of University of Helsinki.

The Department of Mathematics and Statistics (MS) and the Organismal and Evolutionary Biology Research Programme (OEB) jointly invite applications for a

POSTDOCTORAL RESEARCHER

in statistics and machine learning for a fixed term of two years. There will be a trial period of six months in the beginning. The starting date is 1 April, 2020, but a later starting date can be negotiated.

The post doc position is part of the Research Centre for Ecological Change (REC) and is funded by the Jane and Aatos Erkko Foundation and Academy of Finland. PIs of the Centre are prof. Anna-Liisa Laine, prof. Otso Ovaskainen, prof. Tomas Roslin, associate. prof. Marjo Saastamoinen and assist. prof. Jarno Vanhatalo. REC is a consortium of research groups from several departments of the University of Helsinki that operates mainly at OEB.

The overarching aim of REC 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 please visit our website Research Centre for Ecological Change (https://www.helsinki.fi/en/researchgroups/research-centre-for-ecological...).

The statistics and machine learning position is placed at departments of mathematics and statistics and OEB, and 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. For our recent methodological publications, see the reference list at the end.

The successful applicant should have doctoral degree in statistics, machine learning, applied mathematics or other relevant field, and have experience in the development and application of Bayesian methods for computationally challenging problems. Prior experience in ecology is not necessary, but considered an advantage. The exact direction of the work can be agreed upon based on the experience and interests of the candidate.

For more information, contact assistant prof. Jarno Vanhatalo (jarno.vanhatalo(at)helsinki.fi).

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.

Applications should include the following documents as a single pdf file: motivational letter (max 1 page), CV (max 2 pages), and publication list. Please also include contact information of two persons willing to provide a reference letter by separate request.

Please submit your application using the University of Helsinki Recruitment System via the Apply for the position link. Applicants who are employees of the University of Helsinki are requested to leave their application via the SAP HR portal. The deadline for submitting the application is 1 March 2020.

References relevant to the project

Hartmann, M., Hosack, G. R., Hillary, R. M. and Vanhatalo, J. (2017). Gaussian processs framework for temporal dependence and discrepancy functions in Ricker-type population growth models. Annals of Applied Statistics, 11(3):1375-1402.

Itter, I., Vanhatalo, J. and Finley, J. (2019). EcoMem: An R package for quantifying ecological memory. Environmental Modelling & Software, 119: 305-308.

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

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.

Tikhonov, G., Duan, L., Abrego, N., Newell, G., White, M., Dunson, D., and Ovaskainen, O.. 2019. Computationally efficient joint species distribution modeling of big spatial data. Ecology 00( 00):e02929.

Vanhatalo, J., Hartmann, M and Veneranta, L (2019). Additive multivariate Gaussian processes for joint species distribution modeling with heterogeneous data. Bayesian Analysis, doi:10.1214/19-BA1158

Vanhatalo, J., Li, Z. and Sillanpää, M. (2019). A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotype data. Bioinformatics, 35(19):3684-3692.

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.

Due date

01.03.2020 23:59 EET