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U. Strasbourg: Postdoc position in machine learning/data mining


Postdoctoral position available to candidates interested in developing new methods for knowledge reuse in machine learning with the aim of improving understanding of models in different contexts. This position would last for one year, with the possibility for extension.



Reframe project At: U. Strasbourg
Location: Strasbourg, France
Web: www.reframe-d2k.org
Position: Postdoc position in machine learning/data mining

By Nicolas Lachiche, Nov 2014.

One postdoctoral position on versatile models for relational data and unsupervised learning tasks is available at the university of Strasbourg, France,  to perform high-quality research on the REFRAME project (Rethinking the Essence, Flexibility and Reusability of Advanced Model Exploitation) granted under the CHIST-ERA 2011 call to a consortium consisting of the University of Bristol (project coordinator), Polytechnic University of Valencia and the University of Strasbourg.

The overall aim of the project is to develop an innovative and principled approach to knowledge reuse which will allow a range of known machine learning and data mining techniques to anticipate and deal with common contextual changes. The approach is based around the new notion of model reframing, which can be applied to inputs (features), outputs (predictions) or parts of models (patterns), in this way generalising, integrating and broadening the more traditional and diverse notions of model adjustment in machine learning and data mining.

The ultimate goal of the project is to provide a much better understanding of the issues involved in the generation and deployment of a model for different contexts, as well as the development of tools which ease the extraction, reuse, exchange and adaptation of knowledge for a wide spectrum of operating contexts. To find out more about the project, visit http://www.reframe-d2k.org.

The postdoctoral fellow will contribute to works on generalising ROC analysis to new context changes, in particular on relational data and on unsupervised learning tasks. Part of the work will concern the validation campaigns, in particular preparing datasets for challenges, setting up and running experiments, and integrating our algorithms in a collaborative platform.

We are looking for a post-doctoral fellow with a relevant background in data mining or machine learning, with experience in cost-sensitive learning/imbalanced data, or in a related area, such as concept drift, data shift, transfer learning or domain adaptation. An experience on real application or in relational data mining/inductive logic programming/statistical relational learning could be useful. French speaking is not mandatory for work.

The position is available immediately, for 1 year, and could be extended until August 31, 2016.

Applications should be submitted as soon as possible, and will be considered until the position is filled. Enquiries and applications, including CV and references, must be sent to Nicolas Lachiche nicolas.lachiche@unistra.fr.

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