LTH, 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 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 numerics for partial differential equations (PDEs) and machine learning. The successful candidate will thus be part of a highly stimulating and rewarding work environment, with the possibility of contributing to important modern-day problems.

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 can also include teaching and other departmental duties (no more than 20%).The research area for the current call is statistical mechanics and machine learning.

Research Project
Mathematical research of the complex interactions involving many body dynamics has gained momentum with the emergence of novel machine learning methodologies. Applications range from vehicles in road networks to particles interacting in lattice environments or reagents in an epitaxial process in a chemical reactor. In modeling such processes we typically have to account for complicated geometries while interactions can occur at different spatial or temporal scales. As a result to improve our description and eventual understanding of these processes we use ideas from statistical mechanics, differential equations and machine learning. Applications that deal with supervised learning usually leads to large-scale optimization problems.

The aim of the project is to construct mathematical models from statistical mechanics and machine learning in order to describe complex many body interactions in networks. The networks can involve interactants which range from vehicles, in the case of road networks and smart cities, to particles in the case of internet networks or communication grids.

In this project we will construct new methods to analyze behavior, discover and describe emerging patterns and implement them on high-performance systems. At the same time we will process real data in order to both teach our machine learning models but also to evaluate our respective solutions against reality. Since the resulting models can involve a large number of parameters we must assess their relevance and importance.

Admission requirements
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 or relevance to the subject area, of which at least 60 credits from the second cycle and a specialized 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.

Additional requirements:

  • Very good oral and written proficiency in English.
  • Very good programming skills.

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:

  • Programming experience in Python and Tensorflow.

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.

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, grade transcripts and other documents you wish to be considered (contact information for your references, letters of recommendation, etc.).

You are also required to answer the job specific questions as a step in the application process. 

Type of employment Temporary position longer than 6 months
First day of employment 2020-01-01 or according to agreement
Salary Monthly salary
Number of positions 1
Working hours 100%
City Lund
County Skåne län
Country Sweden
Reference number PA2019/3297
  • Alexandros Sopasakis, +46462224440
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 29.Oct.2019
Last application date 26.Nov.2019 11:59 PM CET

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