Doctoral Researcher / Väitöskirjatutkija (2 positions), Deep Learning for Machine and Robot Percept
About this role
Doctoral Researcher / Väitöskirjatutkija (2 positions), Deep Learning for Machine and Robot Perception
Tampere University and Tampere University of Applied Sciences create a unique environment for multidisciplinary, inspirational and high-impact research and education. Our university community has its competitive edges in technology, health and society. www.tuni.fi/en
We are looking for two Doctoral Researchers (PhD students) to work on deep learning methodologies for machine and robot perception. These positions are funded by the Horizon Europe project OPERA (Open Perception, Learning, and Decision-Making Toolbox for General Purpose Robot AI), and the selected applicants will be members of the Computational Intelligence group led by Professor Alexandros Iosifidis, and closely collaborate with the Signal Analysis and Machine Intelligence group led by Professor Moncef Gabbouj on achieving the goals and objectives of the Tampere University team within the project.
The positions are full-time starting from October 1 st , 2026, until the end of the project September 30 th , 2030. In case a selected applicant starts later than the start of the project, the duration of the position will be adjusted to end at the end of the project. The workplace is located at Hervanta Campus of Tampere University in Finland, and the positions are situated in the Computing Sciences Unit of the Faculty of Information Technology and Communication Sciences.
JOB DESCRIPTION
The doctoral researchers at Tampere University will propose and develop novel deep learning methodologies, disseminate their results in high-quality publications in international conferences and journals, interact with project partners in project activities, integrate their methodologies and related baselines to the toolbox of the project. The research topics for the selected applicants are the following:
Doctoral Researcher 1 will work on the following topics:
Self-supervised and complementary learning strategies
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