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Postdoc in Data Science/Deep Learning/Big Data Mining for fraud detection in ULB-MLG Brussels

Country/Region : Germany

Website : https://euraxess.ec.europa.eu/jobs/211909

Description

Applications are invited for full-time post-doctoral (3 years) research position at the Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Belgium.
The vacancy is available to be filled in February (earliest) till April (at the latest). The researcher is expected to carry out fundamental research in Data Science/Machine Learning/Big Data Mining and to take an active role in an applied research project on deep learning for credit card fraud detection which is funding the position.
The Machine Learning Group, ULB is a research group that carries out theoretical research on statistical machine learning, predictive modeling and computational statistics, and takes part in several applied research projects in domains as diverse as big data mining, bioinformatics to biomedical engineering and fraud detection.
Located in heart of Brussels, the Université Libre de Bruxelles is renowned for quality research through its prestigious affiliate research centers and world renowned faculty. Brussels, the capital of Belgium and the European Union, is a cosmopolitan city renowned for its picturesque squares and buildings, serene parks and gardens and a very vibrant cultural life spiced up by many international languages.
Deadline
Applicants are encouraged to apply as soon as possible as positions will remain open until filled by suitable candidates.
Contact
Candidates are invited to send their CV with the detailed list of publications, their PhD thesis and the name of at least 2 reference persons in .pdf format by email to gbonte-AT-ulb.ac.be.
The subject field of your email must include MLG-JOB: research position.
Equal Opportunity Employer
ULB is an equal opportunity employer and is committed to employing more handicapped individuals and especially encourages them to apply. ULB wishes to increase the proportion of women in areas in which they are underrepresented. Women are strongly encouraged to apply.

Last modified: 2017-10-31 10:48:05