Lund University was founded in 1666 and is repeatedly ranked among the world’s top 100 universities. The University has 40 000 students and 7 600 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.
LTH forms the Faculty of Engineering at Lund University, with approximately 9 000 students. The research carried out at LTH is of a high international standard and we are continuously developing our teaching methods and adapting our courses to current needs.
A researcher scientist position is available in the area of computer vision and machine learning, with applications to scene understanding (semantic segmentation, 3d reconstruction, object modeling, object and action recognition, and image categorization) from images and video sequences. The position is funded in part under the European Research Council Consolidator grant SEED and is available for a period of up to 2 years. The approach is strongly research oriented, targeting contributions to be published in high-profile international journals and conferences in computer vision, machine learning and computer graphics. We focus on novel theoretical and algorithmic contributions, but also the design of associated proof-of-concept prototypes.
The successful candidate will be involved in research along one of the above themes in collaboration with Prof. Cristian Sminchisescu and his group members.
- Applicable methods for visual feature extraction based on hierarchical (deep) architectures as well as large-scale numerical optimization and machine learning techniques with a general demonstrator emphasis towards dynamic scene understanding -- relational models of visual scenes containing people interacting with objects.
- Depending on the interest and strengths of the candidate the work can focus on one (or several) of the following aspects: numerical optimization and machine learning algorithms including deep learning and reinforcement learning, flatness and regularization, 2d or 3d modeling, hierarchical feature extraction, computational visual attention mechanisms, semantic segmentation, object recognition and image categorization.
- Possibilities to supervision of degree projects and doctoral students
- Collaboration with industry and wider society
- Administration related to the work duties listed above
- Opportunity for training in higher education teaching and learning shall be given if teaching is part of the work duties.
- Actively seeking external research funding
Applicants must have:
- A PhD or equivalent research qualification within computer vision, mathematics, or machine learning, with applications to scene understanding.
- Very good oral and written proficiency in English.
- A strong applied mathematics or physics background is essential. Solid programming skills including (some of) the following, is necessary: C/C++, Matlab, TensorFlow/Caffee. Evidence of releasing research/scientific software is a plus.
- Applicants for the position must have at least one publication at major computer vision or machine learning top-level international conference of journal including ICCV, CVPR, ECCV, ICLR, ICML, NIPS, PAMI, IJCV, JMLR.
Assessment criteria and other qualifications
Consideration will also be given to good collaborative skills, drive and independence, and how the applicant’s experience and skills complement and strengthen ongoing research within the department, and how they stand to contribute to its future development.
Applications should include a complete original academic records, curriculum vitae, a list of publications and the name of 3 senior researchers who might be contacted for reference letters.
Terms of employment
Temporary position, 2 years.
Instructions on how to apply
Applications shall be written in English and be compiled into a PDF-file containing:
- résumé/CV, including a list of publications,
- a general description of past research and future research interests (no more than three pages),
- contact information of at least three references,
- copy of the doctoral degree certificate, and other certificates/grades that you wish to be considered.
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