PhD student in Computer Science with specialization in Machine Learning

At Mälardalen University people meet who want to develop themselves and the future. Our 15 000 students read courses and study programmes in Business, Health, Engineering and Education. We conduct research within all areas of education and have internationally outstanding research in future energy and embedded systems. Our close cooperation with the private and public sectors enables us at MDH to help people feel better and the earth to last longer. Mälardalen University is located on both sides of Lake Mälaren with campuses in Eskilstuna and Västerås.

At the School of Innovation, Design and Engineering our students are studying to be for example innovators, entrepreneurs, illustrators, communications officers, network technicians and engineers. Here we have the research specialisations of Embedded Systems, and Innovation and Product Realisation. Our work takes place in cooperation with and in strategic agreements with companies, organisations and public authorities in the region.

Employment information
Employment: Temporary employment
Scope: Full time
Closing date for application: 2018-10-15
Campus location: Västeras
School: School of Innovation, Design and Engineering, (IDT)

Position description
Streaming data are gaining particular interest for machine learning research. As these data may arrive at a high speed with open ends, it is practically infeasible to store the full volume of data and then process them in a batch-based manner. Learning of big data streams must be performed incrementally and online to fulfill the constraints on memory usage and computing time. Moreover, since the data-generating process is nonstationary in a dynamic environment, the underlying concept that is hidden in data may evolve with time. Adapting to concept drift to reflect the evolution of the dynamic process presents another crucial challenge in the development of stream mining algorithms.

This Ph.D student project will focus on data analysis and machine learning with evolving data streams in a big data scenario. You will develop new methods and algorithms for smart data discovery as well as incremental learning from complex time series data. A key issue will be how to timely update the smart data and knowledge model in reaction to concept drift. You will perform the research in close collaboration with ABB to apply and test the proposed ideas and techniques in real industrial applications, e.g., the monitoring and real-time analysis of power grids.

The position also includes departmental duties such as teaching at the level of 20%

Qualifications
Only those who are or have been admitted to third-cycle courses and study programmes at a higher education may be appointed to doctoral studentships. For futher information see Chapter 5 of the Higher Education Ordinance (SFS 1993:100).

The applicant should have a Master degree in Computer Science, Computer Engineering, Robotics, Applied Mathematics, Electrical Engineering, or equivalent. Proficiency in English, both written and oral, is required.

Knowledge/experience in artificial intelligence and machine learning is required. Prior knowledge of signal processing is a merit.

Extensive programming experience is required, with knowledge of Java, Python, MATLAB, and big data analysis platforms being of high merit.

Decisive importance is attached to personal suitability. We value the qualities that an even distribution of age and gender, as well as ethnic and cultural diversity, can contribute to the organization.

Application
Application is made online. Make your application by clicking the "Apply" button below.

The applicant is responsible for ensuring that the application is complete in accordance with the advertisement and will reach the University no later than closing date for application.

We look forward to receiving your application.

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