Doctoral student in Applied Mathematics with a focus on Computer Vision

Description of the workplace

The position will be placed in the Division for 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.

Being a doctoral student

As a doctoral student, you are both admitted as a student and employed at Lund University.

As a doctoral student, you will be trained in a scientific approach. In short, you will be trained to think critically and analytically, to solve problems independently using the right methods, and to develop an awareness of research ethics. In addition, you will have the opportunity to work on projects, to develop your leadership and pedagogical skills. Throughout your studies, you will be guided by supervisors. Doctoral studies end with a thesis and a doctoral degree.

More about being a doctoral student at LTH on lth.se. 

Subject and project description

The research area for the current call is computer vision and machine learning, with a focus on methods for three-dimensional reconstruction and navigation from images and video. The research subject is applied mathematics.

The research project focuses on 3D reconstruction methods and navigation in large-scale environments. Emphasis is placed on combining techniques from geometric computer vision and machine learning, to build accurate and reliable reconstructions and maps from visual data. The aim is to develop systems that incorporate natural geometric constraints, such as epipolar geometry and homographies, restricting the observations to lie in a non-linear manifold with modern machine learning techniques, such as physically informed neural networks. 

The position is funded by the Swedish Strategic Research Environment ELLIIT and is part of the project "Learning Geometric Representations".

Work duties

You will primarily devote yourself to your doctoral programme, which includes participation in research projects as well as third cycle courses, seminars and conferences. The work duties will also include teaching and other departmental duties (no more than 20%).

The thesis work 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.

Qualifications

To be eligible for admission and employment as a doctoral student, you must fulfil the requirements below.

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 45 credits from the second cycle.

In addition, the student must be assessed as having the ability required to successfully complete the programme.

Exemptions from the entry requirements may be granted by the Dean of LTH.

Additional requirements

In order to complete the doctoral programme in question, the following are also required:

knowledge and skills relevant to the thesis project and the subject of study.

• good ability to work independently and to formulate and tackle research problems.
• good written and oral communication skills
• good ability to cooperate
• very good knowledge of English, spoken and written

Other qualifications (advantages)

For the doctoral programme in question, the following are considered as other qualifications:

• 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

We offer

Lund University is a public authority which means that employees get particular benefits, generous annual leave and an advantageous occupational pension scheme. It is advisable to enter information on what your unit specifically can offer as a workplace.

More about working at Lund University on lu.se. 

About the employment

The employment is a fixed-term employment at full time, starting 20260901 or as agreed. Third cycle studies at LTH consist of full-time studies for 4 years. In the case of teaching and other departmental duties, the employment is extended accordingly. Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

More about terms of employment for doctoral students on Lund University’s Staffpages. 

How to apply

Applications shall be written in English and include:

• CV and a cover letter stating the reasons why you are interested in the doctoral programme/employment and in what way the research project corresponds to your interests and educational background.
• Copies of issued study certificates and/or awarded degree certificates. These must confirm that you meet the general and specific admission requirements for the doctoral programme and show that you have the subject knowledge required for the doctoral programme project.
• Other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.)

We welcome your application.