The PhD student will work with Gaussian processes for spatial and spatio-temporal environmental data. The application areas include, for example, species distribution modeling and modeling environmental variables such as water quality. The precise content of the work will be tailored according to students background and interest but the work will include aspects from Bayesian statistics and computational methodology (e.g., Markov chain Monte Carlo, expectation propagation and Laplace approximation).
The work will be supervised by Jarno Vanhatalo (http://www.helsinki.fi/~jpvanhat/) and conducted in the New Models and Methods to Fuse Spatial Information from Complementary Sources -research project funded by the University of Helsinki. The abstract of the project is:
A wide range of users of marine areas require access to information on the spatial distribution of species and environmental properties in order to plan strategies and activities on a range of spatial scales. Application areas include, for example, marine spatial planning, conservation planning, and area prioritization in case of environmental disasters. Current development in the Geographic Information System (GIS) technology provides us increasing amount of data and efficient tools to collect and visualize them. However, in many situations of practical interest the available useful data are patchy, sparse or totally missing. In such situations, we need to be able to fuse complementary sources of information, including, e.g., scientific surveys and voluntarily collected citizen science data as well as expert information, in order to build useful knowledge for management. In this project, we will develop methods i) to fuse information from different kinds of data, including, e.g., survey, voluntarily collected observational and censored data, ii) to fuse expert knowledge with various data sets, and iii) to plan future surveys cost efficiently.
The vacancy will be filled starting 15th February, 2015 or as agreed.
The salary will be based on the Finnish universities teaching and research personnels job requirement level 2 (PhD student). In addition, part of the salary will be based on the personal performance. The starting salary will be approximately 2 000 2 200 €/month.
Requirements and applying
The successful applicant should have a Master’s degree or equivalent qualification in statistics, computer sciences, environmental sciences, geoinformatics or other related discipline. Prior experience in Bayesian statistics and computational methodology are considered a strong asset. Further information can be obtained from Jarno Vanhatalo (email@example.com).
The applicants are requested to enclose with their application CV, a description of studies, copy of degree certificate and a motivation letter. Applications should be addressed to firstname.lastname@example.org. The closing date for application is January 31st, 2015.
Site of work
The PhD student will work in the Fisheries and Environmental Management research group (http://www.helsinki.fi/science/fem/) at the University of Helsinki (http://www.helsinki.fi/university/). The University of Helsinki is one of the best multidisciplinary research universities in the world. It is an international academic community of 40,000 students and staff members and it operates on four campuses in Helsinki