An opening is available for an OPEN RESEARCH POSITION at the University of Ljubljana in the area of deep learning for computer vision. The opening is available within a national research project funded by the Slovenian Research Agency (ARRS). The goal of the project is to develop deep learning models able to infer volumetric part-based models from visual data, e.g., depth images, point clouds, intensity images, etc.

The appointment is from January 1st, 2019 (may be delayed a few (1-3) months if needed) until June 30th, 2020 with a possible extension.

If the candidate does not hold a PhD degree, enrollment into the Doctoral School of the University of Ljubljana is also possible.

Work description for Open research Position

The candidate is expected to

• Conduct research in the scope of a fundamental ARRS project
• Develop deep learning solutions for the recovery volumetric models from visual data
• Publish results in peer reviewed vision-oriented journals and conferences
• Collaborate with students and research staff on related projects

Expected qualifications

• MSc or PhD degree in computer science or related fields
• Outstanding programming skills, e.g., Python, Matlab, C++
• Experience with deep learning frameworks, e.g., Keras, PyTorch, Caffe
• Familiarity with computer vision tools and libraries, e.g., OpenCV, VLFeat
• Good track record – publications in SCI indexed journals and top-tier conferences
• Excellent oral and written communication skills (in English)

How to apply

Interested candidates should send a CV with a detailed description of skills and research experience and a cover letter with the e-mail subject [ARRS SQ: Research application] to Assoc. Prof. Peter Peer and Assoc. Prof. Vitomir Struc. Review of applications will continue until the position is filled. Feel free to contact us if you have any questions.

Project web-site

Note: Before you apply for this position, you should have good number of SCI/ SCIE indexed research papers with reference to recent SCI/ SCIE indexed journals list 2018 and/ or review paper. Alternatively, you should have published your research work in core ranked conferences.

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