PhD Position in Probability Theory and Statistics

Lund University was founded in 1666 and is consistently ranked as one of the world's leading universities. It has around 47,000 students and more than 8,800 employees in Lund, Helsingborg, and Malmö. We unite in our efforts to understand, explain, and improve our world and the conditions of human beings.

Lund University welcomes applicants from diverse backgrounds and experiences. Equality, equal opportunities, and diversity are fundamental principles for all parts of our operations.

The School of Economics and Management at Lund University is an internationally renowned business school that holds a so-called triple accreditation. We operate in an international environment and offer sought-after programs at both undergraduate and master's levels. The School of Economics and Management has close collaborations 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.

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

At the Department of Statistics, approximately 15 researchers, teachers, PhD 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).

The department is now seeking candidates for a PhD position in probability theory and statistics.

PhD Position in Probability Theory and Statistics

The Department of Statistics at Lund University is seeking candidates for a PhD 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 the dynamics of cell growth. The research will focus on investigating the design of these models and how to integrate spatial interactions and statistical inference techniques to enhance their accuracy and predictive power. 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 improving computational methods to optimize 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, there are many biological systems where the collective acts more advanced than its simple parts, such as ant colonies solving intricate optimization problems or swarms moving in a way that deters or confuses attackers. The development of mathematical models for systems with collective intelligence represents a significant scientific advancement, particularly through the integration of neural networks within artificial intelligence (AI). Nevertheless, this area remains highly active in research, not least due to the predominantly algorithmic nature of current applications and the incomplete theoretical understanding of these models. This project aims to use interacting particle systems to build simple models that mimic the functions and macroscopic behavior of intelligent adaptive networks. Using modern probability theory and graph theory, the transfer of information between the simple parts that give rise to phenomena of collective intelligence within these systems will be analyzed.

The full advertisement can be found here https://lu.varbi.com/what:job/jobID:804648/ (https://lu.varbi.com/what:job/jobID:804648/)

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