Place of work: Institute of Geomatics and Analysis of Risk (IGAR), University of Lausanne
Education and skills: MS degree in one of the following disciplines: statistics, machine learning, computer science, physics, geosciences, applied mathematics. Candidates should have a sound background in spatio-temporal data analysis using machine learning and geostatistical approaches. Knowledge of scientific programming languages as Matlab, C, or R is important.
Description: IGAR opens a position in the field of application of machine learning algorithms (neural networks of different architectures, support vector machines, etc.) and geostatistics for geo/environmental sciences. The main tasks concern the development, adaptation, and programming of data mining (pattern recognition) models and tools. In particular, topics to be studied include modeling of data clustering, novelty detection, feature selection, manifold learning, risk mapping, and spatio-temporal simulations. In addition to the PhD the candidate is expected to assist in teaching (French, English) and other projects at the institute.
Remarks: Motivation letter, CV, list of publications, MS thesis or equivalent, and recommendation letter should be sent by e-mail to Prof. Kanevski. website link: http://www.unil.ch/webdav/site/igar/shared/pdf_jobs/Post_PhD_IGAR_2010.pdf
Deadline for applications: 10.01.2010
Contact IGAR, Prof. M. Kanevski Mikhail.Kanevski@unil.ch Homepage: http://www.unil.ch/igar
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