PhD student position in topic Intelligent data analysis

Updated: 2 months ago
Job Type: FullTime
Deadline: 20 May 2021

KInIT realizes PhD study in partnership with Faculty of Information Technology, Brno University of Technology.

KInIT doctoral students will be full-time KInIT employees and devote their time to research and study for their PhD degree. At the same time, KInIT doctoral students will act as external students of FIT VUT and graduates will receive their degree from FIT VUT.

Supervising team: Viera Rozinajová (supervisor, KInIT), Sféra, Softec

Key words: data analytics, machine learning, optimization, smart grid, energy sharing

Due to deployment of new technologies in our daily lives, huge amounts of data of various types are generated continuously. These datasets need to be processed efficiently – the information contained in them often supports the correctness and accuracy of decision-making processes. The development of data analysis methods is therefore an important part of IT research. For various reasons, traditional processing methods are not generally applicable, so new approaches need to be sought. They are mostly based on artificial intelligence and machine learning.

The current research is focused on optimization problems in energy domain. Here the renewable energy sources, batteries and electromobiles change the classical one-way centralised electric grid into two-way distributed network. This fact raises a number of research questions and problems to solve, e.g. microgrid optimization. New ways of energy sharing among prosumers need to be proposed, but there are some other research challenges that need to be addressed in this area.

However, the subject of interest can be data analysis in a broader context – considering tasks of prediction, clustering, classification or detection of anomalies in different domains.

Relevant publications:

  • V. Rozinajova, A. Bou Ezzeddine, G. Grmanova, P. Vrablecova, M. Pomffyova. Intelligent Analysis of Data Streams . In: Towards Digital Intelligence Society: A Knowledge-based Approach. Springer, 2021.
  • P. Laurinec, M. Loderer, M. Lucka, V. Rozinajova. Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption . In Journal of Intelligent Information Systems. Vol. 53, iss. 2 2019, 219-239.

Read more at:

View or Apply

Similar Positions