PhD position at the Department of Statistics

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.

Lund University School of Economics and Management is looking for applicants for a doctoral position in probability and statistics.

Doctoral Position in Probability and Statistics

The Department of Statistics at Lund University invites applications for a doctoral position. The selected candidate will work on one of the following two research projects:

A) Mathematical Modeling and Estimation of Cell Growth Using Non-Local Proliferation Models

This project focuses on developing non-local proliferation models using partial differential equations (PDEs) to analyze cell growth dynamics. The research will focus on exploring the formulation of these models and incorporating spatial interactions and statistical inference techniques to enhance their accuracy and predictive capability. Various methodological approaches will be considered to assess their effectiveness in capturing biological variability and uncertainty. The study will involve fitting PDE-based models to experimental or simulated data, evaluating different estimation frameworks, and refining computational techniques to enhance model performance. By integrating mathematical modeling, statistical inference, and computational tools, this research aims to contribute to a deeper understanding of cell proliferation and spatial heterogeneity in biological systems.

B) Adaptive networks exhibiting collective intelligence

In nature, many biological systems can be found in which the collective acts more elaborate than its simple parts, e.g. ant colonies solving intricate optimization problems or swarms moving in a way to deter or confuse a predator. The development of mathematical models for systems exhibiting crowd intelligence represents a significant scientific advancement, particularly the integration of neural networks in artificial intelligence (AI). Nevertheless, this field remains highly active in ongoing research due to the predominantly algorithmic nature of current implementations and the incomplete theoretical understanding of these systems. This project aims at using interacting particle systems to build simple models, mimicking functions and macroscopic behavior of smart adaptive networks, and with help of modern probability and graph theory analyzing the transfer of information among the simple parts, which give rise to phenomena of group intelligence within these systems.

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.

Basis of Assessment                                                                                   

Only those admitted to PhD studies may be appointed to doctoral studentships. The primary selection criterion is the candidate’s ability to successfully complete PhD studies.

For this particular position, a successful candidate should have a strong background in mathematics or a related field, preferably with experience in probability theory and statistics. Further, good knowledge of oral and written English is required. Knowledge about random discrete structures and stochastic algorithms or partial differential equations (PDEs) and mathematical modeling respectively is highly meritorious.

Conditions

Fixed-term employment for four years according to HF Chapter 5 Section 7.

Application

The application should contain the following documents:
A personal letter describing yourself, your specific research interests and your reasons for applying for this position; 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 at least two references from people familiar with your qualifications.

Optionally, if applicable, a candidate can provide a research plan accounting for her/his current interests.

Since we strive for a more equal gender distribution within our department, we encourage both female and male applicants.

Welcome with your application!