Lund University, LTH, Centre for Mathematical Sciences

Lund University was founded in 1666 and is repeatedly ranked among the world’s top universities. The University has around 47 000 students and more than 8 800 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.

Lund University welcomes applicants with diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset.

Description of the workplace

The position will be placed at the division of Computer Vision and Machine Learning at the Centre for Mathematical Sciences. The Centre for Mathematical Sciences is an institution affiliated with both the Faculty of Engineering (LTH) and the Faculty of Science at Lund University. Within the division of Computer Vision and Machine Learning, there are several senior researchers and approximately 20 doctoral candidates. Research in computer vision began in the mid-1980s and currently encompasses (i) Geometry and computer vision (including analysis of video, audio, radio, and radar signals), (ii) Medical image analysis, and (iii) Machine learning/artificial intelligence. The group boasts extensive experience in fundamental research within computer vision, machine learning and artificial intelligence, as well as a track record of translating such findings into practical applications for end-users. The position is funded by the Swedish Research Council and is part of the project "Beyond 3D points in Sparse Visual Mapping".

Research subject

The research area for the current call is computer vision and machine learning, with a focus on methods for visual localization and mapping. The research subject is applied mathematics.

Work duties

The main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects and third cycle courses. The work duties will also include teaching and other departmental duties (no more than 20%).

The research project focuses on 3D mapping methods that support re-localization and navigation in large-scale environments. Emphasis is placed on techniques from geometric computer vision, including structure-from-motion, visual localization, and SLAM, to build accurate and reliable maps from visual data. The aim is to develop systems that are not only robust and scalable but also well-suited for downstream tasks such as motion planning and scene understanding. The thesis will involve designing new methods, conducting experiments, collecting and processing data, programming and implementation, as well as writing scientific publications and presenting results at international conferences.

Admission requirements

A person meets the general admission requirements for third-cycle courses and study programmes if the applicant:

  • has been awarded a second-cycle qualification, or
  • has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or
  • has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

A person meets the specific admission requirements for third cycle studies in Applied mathematics if the applicant has:

  • at least 90 credits of relevance to the subject area, of which at least 60 credits are from the second cycle and include a specialised project of at least 15 credits in the field, or
  • a second-cycle degree in a relevant field

Additional requirements:

  • Very good oral and written proficiency in English.

Assessment criteria

Selection for third-cycle studies is based on the student’s potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Special attention is paid to the following:

  1. Knowledge and skills relevant to the thesis project and the subject of study.
  2. An assessment of ability to work independently and to formulate and tackle research problems.
  3. Written and oral communication skills.
  4. Other experience relevant to the third-cycle studies, e.g. professional experience.

Other assessment criteria:

  • Good programming skills in Python or C++
  • Experience in machine learning frameworks (for example PyTorch)
  • Skills in computer vision or machine learning relevant for the project

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant, through experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme.

We offer

Lund University is a public authority which means that employees get particular benefits, generous annual leave and an advantageous occupational pension scheme. Read more on the University website about being a Lund University employee Work at Lund University.

Terms of employment

Only those admitted to third cycle studies may be appointed to a doctoral studentship. Third cycle studies at LTH consist of full-time studies for 4 years. A doctoral studentship is a fixed-term employment of a maximum of 5 years (including 20% departmental duties). Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

Start date is September - October or by agreement.

How to apply

Applications shall be written in English and include a cover letter stating the reasons why you are interested in the position and in what way the research project corresponds to your interests and educational background. The application must also contain a CV, degree certificate or equivalent, grade transcript and other documents you wish to be considered (contact information for your references, letters of recommendation, etc.).

Welcome to apply!

Type of employment Temporary position
First day of employment September - October or by agreement.
Salary Monthly salary
Number of positions 1
Full-time equivalent 100
City Lund
County Skåne län
Country Sweden
Reference number PA2025/1545
Contact
  • Viktor Larsson, viktor.larsson@math.lth.se
Union representative
  • OFR/ST:Fackförbundet ST:s kansli, 046-2229362, st@st.lu.se
  • SACO:Saco-s-rådet vid Lunds universitet, kansli@saco-s.lu.se, kansli@saco-s.lu.se
  • SEKO: Seko Civil, 046-2229366, sekocivil@seko.lu.se
Published 27.May.2025
Last application date 17.Jun.2025
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