Expected start day in poaition: 01.05.2017/to be agreed
Contract length: 1 year, renewable 1x2 year, maximum 4 years
Activity rate: 100%
Workplace: Institute of Earth Surface Dynamics (IDYST)
The Institute of Earth Surface Dynamics (IDYST), University of Lausanne, invites applications for the PhD position in machine learning application for geospatial data analysis and renewable energy assessment
The main tasks concern the development, adaptation, and programming of machine learning (data mining) methods and tools for geospatial data forecasting and uncertainty quantification. The main application fields deal with environmental, meteorological and renewable energy data. In particular, topics to be studied include data clustering, novelty detection, feature selection, manifold learning, and spatio-temporal simulations. The PhD student will work in close collaboration with the Solar Energy and Building Physics Laboratory, École polytechnique fédérale de Lausanne (EPFL) within the framework of Swiss PNR75 "Big Data" project.
MS degree in one of the following disciplines: applied statistics, machine learning, computer science, geomatics and environmental data science. Candidates should have a sound background in high dimensional data mining and visualization, spatial statistics, time series forecasting. Knowledge and experience of programming in Matlab, and R is required.
We offer a nice place in a multicultural, diversified and dynamic academic environment, opportunities for professional training.
Motivation letter, CV, list of publications, MS thesis, and 2 recommendation letters should be sent by e-mail to Prof. M. Kanevski: Mikhail.Kanevski@unil.ch