项目介绍

2026年5月14日

Lund University was founded in 1666 and for several years has been ranked among the world’s top 100 universities. The University has 46 000 students and more than 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.

Lund University School of Economics and Management is one of eight faculties within Lund University. More than 4 000 students and 450 researchers, teachers and other staff are engaged here in training and research in economic history, business administration, business law, informatics, economics, statistics and research policy.

Lund University School of Economics and Management is accredited by the three largest and most influential accreditation institutes for business schools: EQUIS, AMBA and AACSB. Only just over 100 business schools in the world have achieved this prestigious Triple Crown accreditation.

The Department of Statistics employs about 15 researchers, teachers, doctoral students and other staff. We conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic models, data science and machine learning methods. More information can be found on the department’s website: https://stat.lu.se/en/research.

Doctoral studentship in Statistics with Applications to Genetics

The Department of Statistics invites applications for a doctoral position in statistics. The doctoral student will work on the research project entitled “Quantile Regression Models for Genetic Discovery and Phenotype Prediction”. The project aims to develop scalable statistical and computational tools for modern genome-wide association studies (GWAS) and polygenic risk score (PRS) prediction. The overall aim is to move beyond standard mean-based models and develop quantile-regression methods that can detect heterogeneous genetic effects and predict the full conditional distribution of complex phenotypes.

The project combines theoretical statistics, high-dimensional inference, optimization, and large-scale computing with applications in statistical genetics. The doctoral student is expected to contribute to one or more of the following themes:

• developing scalable quantile regression methods for biobank scale GWAS with population structure and/or related individuals
• constructing quantile-regression-based PRS methods that provide prediction intervals and tail-probability risk summaries
• developing computational strategies such as screening rules, ADMM-based optimization, mixed-model approximations, and conformalized prediction intervals
•  implementing user-friendly software, preferably as open-source tools in R and/or Python.

For more details see: https://stat-lu.github.io/PhdPos_2026/

Job assignment

Doctoral students devote their time primarily to completion of course work and the writing of a doctoral thesis.  The employment as a PhD student is granted on yearly basis and the total period of employment may not exceed 4 years of full-time studies. Departmental duties, i.e. teaching, administration or research not directly connected to the dissertation topic, may amount to a maximum of 20% of full time. When such departmental duties are performed, the length of employment is extended accordingly.

Eligibility/Entry Requirements

General admission requirements

An applicant meets the general admission requirements for third-cycle studies if he or she has

1) obtained a second cycle degree,

2) completed at least 240 credits, including at least 60 second cycle credits, or

3) acquired equivalent knowledge in some other way, in Sweden or abroad.

Specific admission requirements

An applicant is eligible to be admitted to the third-cycle program in statistics if he or she meets the general admission requirements and has

1) completed at least 90 credits in the field of statistics, including an independent project worth at least 15 credits, or has

2) completed 60 second cycle credits in the field of statistics, where the field of statistics besides classical subjects also includes probability theory and modern machine learning methods.

An applicant may also be considered to have fulfilled the specific admission requirements if he or she has acquired equivalent knowledge in some other way, either in Sweden or abroad.  To have completed the master thesis is not a requirement at the time of application. However, candidates must have successfully completed their thesis work before the start of the employment.

Other requirements

A proficient level of English is required in both written and oral communication.

Basis of Assessment                                                                                   

Admission to doctoral studies is based on an assessment of the applicant’s ability to benefit from third-cycle education.

The assessment of the applicant’s overall qualifications will consider:

• quality and content of previous written work, such as the Master’s thesis
• ability to participate actively in the Department’s research environment
• ability to perform independent work
• relevant educational background and grades/grade average

Only those who are or have been admitted to PhD studies may be appointed to doctoral studentships. When an appointment to a doctoral studentship is made, the ability of the student to benefit from PhD studies shall be the primary consideration. In addition to devoting themselves to their studies, those appointed to doctoral studentships may be required to work with educational tasks, research, and administration, in accordance with specific regulations in the ordinance.

The selection process will primarily consider the applicant’s ability as shown by competence in statistics and computational methods, and potential to develop independent research.

Application

The application should contain the following documents:

• A personal letter describing yourself
• A curriculum vitae
• A certified copy of grades and degree certificates
• Copies of relevant work such as bachelor or master thesis and articles that you have authored or co-authored
• Contact information for one references from people familiar with your qualifications.
• Any additional documents relevant to the application

Lund University welcomes applicants with diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset.

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