Study with leading statisticians at a world-class university
Applications for entry 2026/27 are open.
Funding deadlines: 15 January 2026 (Applications received by this date will be considered for available studentships; we may also be able to consider applications received by the end of March for funding, but this is not guaranteed.
Our doctoral programme is a three- to four-year research programme, beginning with one year of advanced coursework and culminating in the submission of a thesis that contributes original research in areas such as, but not limited to, data science, applied probability, social statistics, time series analysis and statistical learning.
The Department of Statistics at LSE has long been home to some of the world’s most renowned and innovative statisticians and data scientists. With four research groups—Data Science; Probability in Finance and Insurance; Social Statistics; and Time Series and Statistical Learning—our research spans a wide range of areas, including artificial intelligence, machine learning, statistical inference, quantitative finance, financial statistics and statistical methods applied in the economic and social sciences. Faculty members often contribute to more than one area, and much of the research is interdisciplinary.
Programme Structure
Initially, all students are registered as MPhil in Statistics. In the first year, you will attend taught courses to enhance your background knowledge and research skills, and you will present your research topic annually at the Department’s presentation event. Progression to the second year depends on passing the exams for the taught courses.
Within the first two years (or three years for part-time students), you will undergo an upgrade review to PhD, which typically involves discussing and assessing your research progress to date with two assessors.
Your PhD thesis is usually submitted in the fourth year, followed by a viva examination.
Additional Training and Support
In addition to the compulsory taught courses, there are a variety of other training opportunities, both academic and non-academic. Our three distinct departmental seminar series--Statistics and Data Science; Joint Econometrics and Statistics; Joint Risk & Stochastics and Financial Mathematics -- are open for all, and the PhD reading group enables discussion of topics both within and beyond students’ areas of expertise. The London Taught Course Centre (LTCC) offers five-week or short intensive courses on diverse topics, with attendance generally sponsored by our department. We also encourage first-year students to take courses offered by the Academy for PhD Training in Statistics (APTS).
Other support includes, but is not limited to:
LSE Digital Skills Lab: Provides workshops on a range of coding and technical tools, both in person and online. They also offer dissertation drop-in sessions that can provide technical assistance for your thesis writing.
LSE Library: Offers extensive digital collections and provides terminal access to restricted data sets.
LSE Careers: Organises alumni events, provides personal career advices, and supports career-related writings and job interview preparation.
Teaching Experience
Class teaching plays a valuable role in the Department and is an important part of your training. From your second year onwards, you should expect to teach a minimum of two class groups. You would typically begin by teaching our first-year undergraduate courses, ST102 Elementary Statistical Theory and ST107 Quantitative Methods. Other courses may be available to those who have already gained some teaching experience.
Supervision and Other Resources
You will have a first supervisor and a second supervisor. The second supervisor provides additional or complementary expertise, offers local support if your primary supervisor is unavailable, and serves as a backup to cover contingencies such as illness. Full-time students have at least three supervision meetings each term, while part-time students have at least two meetings per term, with any further arrangements agreed between you and your supervisors.
You will be provided with a computer and desk space in shared offices within the Department. Our departmental Leverhulme Library serves as a repository for useful reference books, as well as a space for meetings and social gatherings.
Entry Requirements
Applicants for our doctoral programme should have achieved a UK First-Class Honours degree in a subject with substantial quantitative content, such as mathematics, statistics, or computer science.
Candidates who do not meet the above criteria but have outstanding achievements (for example, through previous work or research) may still be considered and are encouraged to apply. For further information, please contact our PhD Programme Manager.
Research in the data science area is concerned with the development of new machine learning and statistical methods, and their applications. The areas of applications include the design of novel methods for understanding user behaviour, analysis of social data, modelling and inference for information cascades and epidemic processes that arise in social networks and biomedical applications, as well as algorithms for development of next-generation artificial intelligence systems.
Possible areas of research include:
Bayesian inference and predictions
Functional data analysis
High-dimensional statistics
Machine and statistical learning for relational data
Network data models, inference and predictions
Optimisation and machine learning
Reinforcement learning
Statistical learning methods in precision medicine
Statistical models and inference for ranking data
Stochastic models of epidemic processes
Stochastic optimisation methods
Stochastic processes in econometrics and finance
For more information about potential supervisors and their areas of interest, visit the .
PhD research in probability in finance and insurance encompasses many aspects of the discipline. Methodological and theoretical research is mainly guided by applications with the aid of both academic and industrial experts, covering topics of modern stochastic finance with an emphasis on insurance and financial mathematics. Applications include pricing and hedging exotic products, counterparty risk, portfolio optimisation, risk management and insurance, risk transfer and securitisation, etc.
Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary.
Suggested research areas of PhD research projects include:
Energy markets
Excursions of Lévy processes and applications in finance and insurance
Financial market with frictions
Information asymmetry
Interface between insurance and finance
Lévy processes
Optimal stopping
Point processes in insurance and credit risk
Quantile options and options based on occupation times
Stochastic analysis and its applications in financial mathematics
Stochastic control and analysis of partial differential equations in mathematical finance
This list is indicative only and by no means exhaustive. For more details about supervisors and their areas of research interests, please see the . You will find links to the web pages of individual members of staff here. If you are interested in applying to undertake PhD research in probability in finance and insurance, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal.
PhD programmes of study in social statistics typically include both methodological development and the application of statistical methods to a social science field or to address new developments in social data, such as in sample surveys or social networks. Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary.
Possible areas of research include:
Analysis of complex survey data
Disclosure risk assessment and statistical disclosure control
Estimation from survey data (and related data), taking account of nonresponse and using auxiliary information
Latent transition and latent class models for modelling diagnostic tests
Latent variable models and structural equation models for categorical data
Longitudinal data analysis, especially event history (survival) analysis and dynamic panel models
Modelling response strategies and detection of outliers in educational and behavioural sciences
Multilevel simultaneous equations modelling of correlated social processes
For more details about potential supervisors and their areas of interest, visit the . If you are interested in applying to undertake PhD research in social statistics, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal.
PhD research in time series and statistical learning encompasses many aspects of these disciplines. We are keenly involved in both theoretical developments and practical applications. Current areas of interest include time series (including high-dimensional and non-stationary time series), data science and machine learning, networks (including dynamical networks), high-dimensional inference and dimension reduction, statistical methods for ranking data, spatio-temporal processes, functional data analysis, shape-constrained estimation, multiscale modelling and estimation and change-point detection.
Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary.
Suggested PhD research areas include:
Automating statistical advice
Change detection for complex data
Dimension reduction and factor modelling
Estimation of stochastic volatility models
Financial econometrics
Functional data analysis including functional time series
High-dimensional time series analysis
High-dimensional variable selection
Infectious disease modelling
Inference for sequential data including change detection in multiple data streams
Network time series analysis
Nonparametric and semiparametric regression
Non-stationary time series analysis
Reinforcement learning for time-dependent data
Robust statistical analysis for high-dimensional data
Shape-constrained methods
Spatial econometrics modelling
Spatio-temporal modelling
Statistical analysis of high-dimensional multi-type recurrent events
