Doctoral student in Mathematical Statistics- Sentio Competence Center focusing on Robust Time-Frequency Analysis of Sensor Array Data - Hiring in process/Finished, not possible to apply

Description of the workplace

The workplace is the division of Mathematical Statistics with approximately 25 teachers, researchers and doctoral students. Research areas within mathematical statistics are probability theory and statistical theory. The main task of probability theory is to develop mathematical models for the description and analysis of random processes, and to study the mathematical properties of such models. Within the statistical theory, principles and methods are studied in order to build and test the models with the help of empirical facts and data. Applications are found in all areas of society with an emphasis on science, technology, medicine and economics.

The project is carried out within Sentio – Integrated Sensors and Adaptive Technology for Sustainable Products and Manufacturing – which is a newly inaugurated competence centre coordinated by Lund University in close collaboration with companies from Swedish industry, RISE, Region Skåne and trade associations. The goal is to create a world leading highly interdisciplinary research centre that brings together the wide range of diverse competences from industry and academia that are needed to develop integrated sensor technology with the capacity to significantly improve sustainability, reduce the use of resources, and increase competitiveness across industrial sectors of Sweden.  In this first recruitment Sentio will hire 7 doctoral students within different areas of expertise who will all work together with the research within Sentio. Sentio is financed by Vinnova – Sweden’s innovation agency.

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 research project is performed in close collaboration with other doctoral students within Sentio Compentence Center and involved industry partners. This project aims for robust feature extraction of noisy time-varying data from sensor arrays, e.g. measurements of vibrations within high levels of noise. The main task is to develop robust and real-time efficient estimation methods for time-frequency analysis based on given modeling restrictions. Other parts of the work include development of cleaning procedures for the noisy and corrupted data, choices of optimal sets of sensors and extraction of optimal features using techniques in data mining and machine learning. The work duties will also include teaching and other departmental duties within Mathematical Statistics (no more than 20%).

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 Mathematical Statistics if the applicant has:

• at least 90 credits of relevance to the subject, of which at least 60 credits from the second cycle and a specialized project of at least 30 second-cycle credits in the subject, or
• a second-cycle degree in a relevant subject

Finally, the student must be judged to have the potential to complete the programme. 


Additional requirements:

• at least one course in Programming
• at least one 2nd cycle course in Stochastic processes, Machine learning, or related subjects such as: Time-series analysis, Spatial statistics, Spectral analysis, Statistical learning
• 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:

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.

Other assessment criteria:

• ability (shown via, e.g., a thesis project) to develop, implement, and apply relevant scientific statistical models to data and critically assessing the results
• experience in signal processing and time series analysis
• programming experience (preferably in Julia, Python, Matlab, or R).

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. The candidate is expected to learn Swedish within 3 years. If needed, a language support plan will be devised at the time of employment.

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 §§.

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

Welcome to apply!