Doctoral student in Mathematical Statistics Full-time

Doctoral Student In Mathematical Statistics

Lund University was founded in 1666 and is repeatedly ranked among the world's top universities. The University has around 46,000 students and 8,500 staff based in Lund, Helsingborg, and Malmö. We are united in our efforts to understand, explain, and improve our world and the human condition.

Description Of The Workplace

The position will be based in the Division of Mathematical Statistics at the Centre for Mathematical Sciences. The Centre for Mathematical Sciences is a joint institution for the Faculty of Engineering (LTH) and the Faculty of Science (N) at Lund University, bringing together all mathematical sciences. The division has around 25 employees, evenly divided between senior researchers and doctoral students. Current research areas within the Division of Mathematical Statistics include stochastic models, statistical signal processing, statistical theory, and computational statistics, and probability theory, with applications in areas such as medicine, environmental research, and financial mathematics. We attach great importance to a good, collegial working environment. The department is international with a relatively even gender distribution among employees.

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.

Subject And Project Description

The aim of this project is to develop novel methods for controlling hearing assistive devices using electroencephalography (EEG) data, and behavioral signals. This is a challenging problem, and one of the main objectives will therefore be to develop fast and robust estimation methods for the key cognitive measures and characterizations of the auditory scene. A particular focus will be on obtaining better models of the noise in EEG data by allowing more realistic heavy-tail distributions instead of the more limited Gaussianity assumptions that are commonly used today. Using the improved noise models, machine learning methods will be used to enhance the segmentation of EEG data into auditory signal and background activity allowing for refined control of the hearing aids. The project is interdisciplinary and builds on existing collaborations with automatic control researchers and a newly hired PhD student at Linköping University as well as auditory systems and neuroscience researchers at Eriksholm Research Centre (part of Oticon A/S).

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 within Mathematical Statistics (no more than 20%).

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 mathematical statistics if the applicant has:

• at least 90 credits of relevance to the subject area, of which at least 45 credits are from the second cycle.

Additional Requirements

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

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

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

• ability (shown via, e.g., a thesis project) to develop, implement, and apply relevant statistical methods to data and critically assessing the results.
• experience in stochastic processes, spectral analysis, optimization, statistical machine learning.
• programming experience (preferably in Python, Matlab, R, or similar)

We Offer

Lund University is a public authority which means that employees get particular benefits, generous annual leave, and an advantageous occupational pension scheme.

About The Employment

The employment is a fixed-term employment at full time, starting 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 §§.

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.