Postdoctoral researcher in the area of Information Technology, specialisation Data Mining at The Center for Applied Intelligent Systems Research

The Center for Applied Intelligent Systems Research http://caisr.hh.se/ at Halmstad University, Sweden, is currently looking for a Postdoctoral researcher in the area of Information Technology, specialisation Data Mining, to work within our research projects together with multiple industrial and public administration partners.
The deadline for applications is 16th of October 2016. The position is for 2 years, funded by two projects, BIDAF Big Data Analytics Framework for a Smart Society (a distributed research environment of Halmstad University, SICS Swedish ICT, Hskolan i Skde) and ARISE (collaboration between Halmstad University and Volvo Technology).
More information is available at http://islab.hh.se/OpenPositions
The BIDAF project aims to significantly further the research within massive data analysis, by means of statistical machine learning, in response to the increasing demand of retrieving value from data in all of society. Our research focuses on scalable algorithms that can leverage the distributed framework for efficient mining of knowledge from transient data streams. In particular, we aim to move from algorithms designed to exploit limited amounts of data for as much knowledge as possible towards algorithms designed to process large amounts of data efficiently, build models that are constrained in size, and provide end users with easy to understand and traceable results.
The ARISE project aims to develop algorithms for early detection and analysis of vehicle quality issues, integrating multiple available data sources. New telematics solutions allow monitoring trucks in operation, combining on-board data with existing in-office knowledge such as warranty claims, technical reports and expert knowledge. We will provide quality analysts with data mining and machine learning methods capable of extracting patterns and finding trends in these diverse data sources.
The main way of extracting value from data is to capture the interesting aspects of it using a suitable model. The model is then used for detecting anomalies and trends, analysing key values, or making predictions. In the big data setting, however, one can create not one, but many useful models, focusing on different aspects of the data. We will develop new algorithms for building such sets of models and for ensuring sufficient diversity among them, as well as ways to combine them in flexible ways, for example into hierarchical structures of concepts and sub-concepts, or along time axis to distinguish permanent and time-limited patterns.
The unprecedented amount of data accessible today allows ML to focus on more descriptive and explanatory analysis. Users no longer pose well-formulated, concrete questions, but instead require the system to be capable of highlighting interesting aspects such as deviations, anomalies, relations and co-occurrences. It is almost effortless to generate data, while the cost of analysing it does not change. We will support continuous learning model, where the training and usage is not easily separated, and the system improves its performance all the time, taking advantage of new data as it arrives.
An important aspect of the position is to find connections to other projects within CAISR and on identifying common problems and finding solutions applicable across multiple domains.
For more information about the positions, projects and how to apply, visit visit http://islab.hh.se/OpenPositions or contact Slawomir Nowaczyk (slanow-AT-hh.se) and Antanas Verikas (Antanas.Verikas-AT-hh.se)