Vacant job

Lund University was founded in 1666 and is consistently ranked among the world's leading universities. We have approximately 46,000 students and 8,500 employees in Lund, Helsingborg, and Malmö. We are united in our strive to understand, explain, and improve our world and the conditions of human life.

The Faculty of Economics is one of eight faculties within Lund University. Over 4,000 students and approximately 450 researchers, teachers, and other staff study and work here with education and research in economic history, business administration, commercial law, informatics, economics, statistics, and research policy.

The Faculty of Economics at Lund University is an internationally prominent business school with the so-called triple accreditation. We work in an international environment and offer sought-after programs at the bachelor's, master's, and doctoral levels. The Faculty of Economics has close cooperation with companies and authorities both nationally and internationally. We care about a good, stimulating work environment and continuously work to develop as an attractive employer.

At the Department of Statistics, approximately 15 researchers, teachers, doctoral students, and other staff work. We conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatio-temporal models, non-Gaussian modeling, applied research in social science subjects, as well as stochastic models, computational statistics, and machine learning. More information can be found on the department's website: http://www.stat.lu.se/forskning (http://www.stat.lu.se/forskning)

Doctoral Student in Statistics with Applications in Genetics

The Department of Statistics is advertising a doctoral position in statistics. The doctoral student will work within the research project "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 (Genome-Wide Association Studies (GWAS)) and prediction using polygenic risk scores (PRS). The overarching goal is to move beyond standard models based on means and develop quantile regression methods that can detect heterogeneous genetic effects and predict the entire conditional distribution for complex phenotypes.

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

• development of scalable quantile regression methods for GWAS at biobank scale with population structure and/or related individuals;
• construction of PRS methods based on quantile regression that provide prediction intervals and risk summaries for tail probabilities;
• development of computational strategies such as screening rules, ADMM-based optimization, approximations with mixed models, and conformalized prediction intervals;
• implementation of user-friendly software, preferably as open-source tools in R and/or Python.

For more information, see: https://stat-lu.github.io/PhdPos_2026/ (https://stat-lu.github.io/PhdPos_2026/)

Responsibilities

Doctoral students mainly devote their time to completing coursework and writing a doctoral thesis. Employment as a doctoral student is granted on an annual basis, and the total employment period must not exceed four years of full-time studies. Departmental service, i.e., teaching, administration, or research not directly related to the thesis subject, may comprise at most 20 percent of full-time. When such departmental service is performed, the employment period is extended accordingly.

Qualifications

General Admission Requirements

Basic eligibility for higher education at the advanced level is held by those who have

• completed an advanced level degree, or
• fulfilled course requirements of at least 240 university credits, of which at least 60 university credits at the advanced level, or
• in some other way, within or outside the country, acquired substantially equivalent knowledge.

(Higher Education Ordinance Chapter 7, Section 39)

Specific Admission Requirements

An applicant is eligible to be admitted to the doctoral program in statistics if they meet the general eligibility requirements and have

1) completed at least 90 university credits in the subject area of statistics, including an independent project worth at least 15 university credits, or have

2) completed 60 university credits in the subject area of statistics, where the subject area of statistics, in addition to classical subjects, also includes probability theory and modern methods within machine learning.

An applicant may also be considered to have met the special admission requirements if they have acquired equivalent knowledge in some other way, either in Sweden or abroad. Having completed the master's thesis is not a requirement at the time of application. However, candidates must have completed their thesis work with a passing grade before the start of employment.

Good knowledge of English is required, both in written and oral communication.

Assessment Criteria

Admission of doctoral students is based on an assessment of the applicant's ability to benefit from higher education at the advanced level.

The applicant's general competence:

• quality and content of previous written works, such as a master's thesis,
• ability to actively participate 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 higher education at the advanced level may be employed as doctoral students. When employed as a doctoral student, the applicant's ability to benefit from the doctoral education should be the primary assessment criterion. In addition to pursuing their studies, those employed as doctoral students may be required to work with education, research, and administration, in accordance with special provisions in the ordinance.

Application Procedure

Please visit https://lu.varbi.com/what:job/jobID:934816/ (https://lu.varbi.com/what:job/jobID:934816/) to read the full advertisement.