Lunds Tekniska Högskola, Matematikcentrum, Matematik LTH

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.

The development of computer vision, machine learning and robotics has come a long way but there remains a lot of work to merge the technologies into a functioning integrated system to give us truly smart robots. The use of robotic computer vision today is limited to very specific scenarios or controlled laboratory environments. In the interdisciplinary project Semantic Mapping and Visual Navigation for Smart Robots, which recently has received funding from the Swedish Foundation for Strategic Research, we rely on expertise from computer vision, machine learning, automatic control and optimization in order to take the current state-of-the-art in autonomous systems to the next level of perception, cognition and navigation, and towards key capabilities of robots able to effectively act in the real world. For demonstration, the project will develop an autonomous system for the visual inventory inspection of a supermarket using small-scale, low-cost quadcopters. The system will provide a complete solution for visual navigation and 3D mapping where not only scene geometry is modelled, but also semantic constraints are integrated. The research is relevant for many industrial applications, such as self-driving cars, unmanned ground vehicles, scene modeling and inspection in general. The main goal of the project is to develop novel methods for scene understanding (in terms of semantic structure from motion) and localization using both vision and other sensors. This typically involve robust estimation techniques, structure from motion estimation and machine learning.



Work duties

The main duties involved in a post-doctoral posistion is to conduct research. Teaching may also be included, but up to no more than 20% of working hours. The position shall include the opportunity for three weeks of training in higher education teaching and learning. 

Detailed description of the work duties, such as:

  • Research within the subject area.
  • Teaching in the first, second and third cycles of studies.
  • Supervision of degree projects and doctoral students.
  • Actively seeking external research funding.
  • Collaboration with industry and wider society.
  • Administration related to the work duties listed above.

Qualification requirements

Appointment to a post-doctoral position requires that the applicant has a PhD, or an international degree deemed equivalent to a PhD, within the subject of the position, completed no more than three years before the last date for applications. Under special circumstances, the doctoral degree can have been completed earlier. 

Additional requirements:

  • Very good oral and written proficiency in English.
  • Experience of research within computer vision and machine learning.

Assessment criteria and other qualifications

This is a career development position primarily focused on research. The position is intended as an initial step in a career, and the assessment of the applicants will primarily be based on their research qualifications and potential as researchers. Particular emphasis will be placed on research skills within the subject.

For appointments to a post-doctoral position, the following shall form the assessment criteria:

  • A good ability to develop and conduct high quality research.
  • Teaching skills.

Additional assessment criteria:

  • Papers published in subject relevant journals and conference series, e.g., CVPR, ICCV, ECCV, ICML and NIPS.
  • Skills in developing structure from motion algorithms.
  • Skills in deep learning research. 

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.

Terms of employment

This is a full-time, fixed-term employment of a maximum of 2 years. The period of employment is determined in accordance with the agreement “Avtal om tidsbegränsad anställning som postdoktor” (“Agreement on fixed-term employment as a post-doctoral fellow”) between Lund University, SACO-S, OFR/S and SEKO, dated 4 September 2008.

 Instructions on how to apply

Applications shall be written in English. Please draw up the application in accordance with LTH’s Academic qualifications portfolio – see link below. Upload the application as PDF-files in the recruitment system. Read more:

Type of employment Temporary position longer than 6 months
Contract type Full time
Number of positions 1
Working hours 100
City Lund
County Skåne län
Country Sweden
Reference number PA2018/3085
  • Karl Åström, +46462224548
  • Eskil Hansen, +46462229628
Union representative
  • OFR/ST:Fackförbundet ST:s kansli, 046-222 93 62
  • SACO:Saco-s-rådet vid Lunds universitet, 046-222 93 64
  • SEKO: Seko Civil, 046-222 93 66
Published 19.Sep.2018
Last application date 31.Oct.2018 11:59 PM CET

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