Lund University, Faculty of Engineering, Centre for Mathematical Sciences
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 position will be placed at the Division of Mathematics LTH and Numerical Analysis at the Centre for Mathematical Sciences. The Centre currently has strong research environments in computer vision and machine learning. The position is funded by Wallenberg AI, Autonomous Systems and Software Program (WASP) which is Sweden’s largest individual research program, and provides a platform for academic research and education, fostering interaction with Sweden’s leading companies. WASP will strengthen, expand, and renew the national competence through new strategic recruitments, a challenging research program, a national graduate school, and collaboration with industry. Read more at:https://wasp-sweden.org/
The PhD student will be employed by Mathematics LTH but also be a part of the WASP-AI graduate school and thereby gain access to specialized courses on AI and machine learning. The project is a collaboration between LTH and Chalmers and the PhD student will have an assistant supervisor at Chalmers.
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 can also include teaching and other departmental duties (no more than 20%). The research area for the current call is optimization, computer vision and machine learning.
Machine learning with deep networks have been proven to be powerful tools for many problems in computer vision. Their ability to recognize and categorize image objects have recently undergone a tremendous development with the availability of huge amounts of labeled data. However, pure deep learning methods are highly dependent on data, and the internal operation of deep networks are to a large extent still not understood and in many cases regarded as a “black box”. In this project we are interested in developing methods that combine traditional (parametric) mathematical formulations induced by domain expertise with (non-parametric) models learned from examples.
An example application that we are interested in addressing is 3D-reconstruction and understanding of dynamic scenes from image data. While reconstruction of rigid objects can be done using physically motivated factorization models the corresponding dynamic problem is much less constrained since general object deformations are hard to model. Combining factorization models with learned priors will lead to significantly more stable methods and accurate solutions.
A person meets the general admission requirements for third-cycle courses and study programmes if he or she:
- 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 mathematics if he or she has
- at least 90 credits of relevance to the subject area, of which at least 60 credits from the second cycle and a specialised project of at least 30 second-cycle credits in the field, or
- a second second-cycle degree in a relevant subject.
In practice this means that the student should have achieved a level of knowledge in mathematics that corresponds to that of a Master of Science programs in Engineering Mathematics or Engineering Physics or a Masters degree in mathematics or applied mathematics.
- Very good oral and written proficiency in English.
- A project-relevant master’s thesis.
- Programming skills.
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
- Knowledge and skills relevant to the thesis project and the subject of study.
- An assessment of ability to work independently and to formulate and tackle research problems.
- Written and oral communication skills.
- Other experience relevant to the third-cycle studies, e.g. professional experience.
Consideration will also be given to good collaborative skills, drive and independence, and how the applicant, through his or her experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme.
- Skills in Computer Vision and/or Machine Learning.
- Knowledge about Mathematical Optimization
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 §§.
Instructions on 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, the applicant’s master thesis, and other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.). You are also required to answer the job specific questions as the first step of the application process.
|Type of employment||Temporary position longer than 6 months|
|First day of employment||As soon as possible|
|Number of positions||1|
|Working hours||100 %|
|Last application date||20.Feb.2020 11:59 PM CET|