D'Math University | Statistics & Data Science  ·  Programme #13

PhD Statistics

A world-class doctoral programme spanning 3 to 5 years, offering candidates the environment and supervision to produce original, internationally recognised contributions to statistical science. Four research clusters span from theory to applied genomics and econometrics.

Doctoral 3–5 Years Research International
100+
Active Research Areas
£75k+
Avg Post-PhD Salary
35+
PhD Supervisors
4
Research Clusters

Programme Overview

Our PhD in Statistics is structured around four internationally recognised research clusters: (1) Statistical Theory and Inference, (2) Applied and Computational Statistics, (3) Biostatistics and Genomic Science, and (4) Financial and Econometric Statistics. Doctoral candidates complete an independent thesis under close supervision, contribute to cluster seminars, and are encouraged to publish and present at international conferences from Year 1.

All PhD candidates receive full research stipends, travel grants for conferences, and access to D'Math's high-performance computing cluster for simulation-intensive research.

What You'll Learn

  • Research Methodology: Literature review, problem formulation, and thesis writing
  • Advanced Probability: Measure theory, stochastic calculus, martingale theory
  • High-Dimensional Inference: Minimax theory, sparsity, penalised estimation
  • Bayesian Nonparametrics: Dirichlet processes, Gaussian processes, posterior consistency
  • Clinical & Genomic Statistics: Survival models, GWAS, single-cell data
  • Causal Inference: Potential outcomes, instrumental variables, DAGs
  • Computational Methods: MCMC, variational inference, HPC workflows
  • Academic Communication: Journal publication, grant writing, conference presentation
Research Areas
🧮

Statistical Theory

Foundational work on optimal estimation, testing, and decision theory — from finite to infinite-dimensional parameter spaces.

🌍

Spatial Statistics

Kriging, geostatistics, spatial point processes, and models for environmental and ecological data.

📊

High-Dimensional Data

Lasso, ridge, elastic net, compressed sensing, and random projection methods for p >> n problems.

🤖

Statistical Machine Learning

Statistical guarantees for neural networks, PAC learning, VC dimension, and generalisation bounds.

🧬

Genetic Statistics

Genome-wide association studies, polygenic risk scores, population stratification, and linkage disequilibrium modelling.

🏥

Clinical Biostatistics

Adaptive trial design, interim analysis, missing data, and regulatory statistics for pharmaceutical research.

📉

Financial Econometrics

High-frequency data analysis, volatility modelling, co-integration, and risk measure estimation.

🔬

Causal Inference

Potential outcomes framework, propensity scores, difference-in-differences, and structural causal models.

Course Catalogue

Click any course to view its objective and learning outcomes.

PST 701 Research Methods in Statistics +

Objective

To prepare doctoral candidates for statistical research.

Learning Outcomes

  • Apply rigorous research design.
  • Use specialised databases.
  • Apply LaTeX writing.
  • Critique published research.
  • Write proposals.
PST 702 Advanced Statistical Theory +

Objective

To master advanced statistical theory.

Learning Outcomes

  • Apply asymptotic theory.
  • Use empirical processes.
  • Apply U-statistics.
  • Use semiparametric theory.
  • Discuss high-dimensional inference.
PST 703 Bayesian Statistics Research +

Objective

To research advanced Bayesian methods.

Learning Outcomes

  • Apply nonparametric Bayesian methods.
  • Use MCMC and HMC.
  • Apply variational inference.
  • Use Bayesian optimisation.
  • Discuss approximate inference.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
PST 704 Statistical Machine Learning +

Objective

To research statistical foundations of ML.

Learning Outcomes

  • Apply concentration inequalities.
  • Use empirical risk minimisation.
  • Apply kernel methods.
  • Use Gaussian processes.
  • Discuss deep learning theory.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a = 1.0 b = 0.5 c = -0.3 d = 1.0
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
Interactive Activity — Gradient Descent on a 2D Loss Surface
Click anywhere on the surface to drop a starting point. Animation traces the descent path on the chosen loss function. Adjust the learning rate to see how step size affects convergence.
Loss: η = 0.10
Click on the loss surface to drop a starting point.
Interactive Activity — Linear Classifier Decision Boundary
Click to add red or blue points. Adjust weights w₁, w₂, b to position the decision line w₁x + w₂y + b = 0. Misclassified points highlight red.
w₁ = w₂ = b =
PST 705 Specialisation Module +

Objective

To pursue research in chosen specialisation.

Learning Outcomes

  • Master a specialised field.
  • Apply field-specific methods.
  • Engage with current literature.
  • Develop specialised skills.
  • Contribute original work.
Interactive Activity — Polynomial Division
Type two polynomials. The activity performs polynomial long division step-by-step.
f(x) = ÷ g(x) =
Interactive Activity — Modular Arithmetic Clock
A "clock" with n positions visualises ℤ/nℤ. Click any position to add to the running total — the clock hand wraps around.
n = 12
Running total: 0 (mod 12)
PST 706 Doctoral Seminar +

Objective

To engage with current research.

Learning Outcomes

  • Present and critique papers.
  • Engage with international research.
  • Participate in peer review.
  • Build a network.
  • Develop presentation skills.
PST 707 Teaching Practicum +

Objective

To develop teaching skills.

Learning Outcomes

  • Plan and deliver lectures.
  • Design assessments.
  • Apply pedagogical theory.
  • Mentor undergraduates.
  • Engage in curriculum design.
Interactive Activity — Truth Table Builder
Type a logical expression using p, q, r and operators (AND, OR, NOT). The truth table generates instantly.
Operators: AND OR NOT XOR -> <->
PST 708 PhD Thesis I +

Objective

To produce original research.

Learning Outcomes

  • Identify an original problem.
  • Conduct literature review.
  • Develop methodology.
  • Produce preliminary results.
  • Present at conferences.
PST 709 PhD Thesis II +

Objective

To advance the research.

Learning Outcomes

  • Develop original methodology.
  • Generate findings.
  • Publish in journals.
  • Develop thesis structure.
  • Defend methodology.
PST 710 PhD Thesis III +

Objective

To consolidate research.

Learning Outcomes

  • Write 80,000-100,000 word thesis.
  • Synthesise contributions.
  • Defend viva voce.
  • Publish multiple articles.
  • Contribute to the field.
Career Pathways
🎓

Professor of Statistics

Lead a university statistics department, teaching advanced courses and directing a productive research group.

🤖

Principal Data Scientist

Head data science research functions at major technology companies including Google, Meta, and Amazon.

🏥

Clinical Statistician

Lead statistical methodology for pivotal clinical trials at top pharmaceutical or biotech companies.

🏛️

Government Chief Statistician

Direct national statistics offices such as ONS, Statistics Canada, or the Australian Bureau of Statistics.

🔬

Research Director

Lead statistical research at major think tanks, central banks, or international organisations like the WHO.

💹

Quant Researcher

Apply statistical theory to develop systematic trading strategies and risk models at hedge funds.

Top Universities Offering This Programme
Stanford University Harvard University Columbia University University of Chicago University College London University of Cambridge University of Toronto NUS Singapore University of Auckland University of Göttingen

Why D'Math University

01

World-Class Supervisors

35+ supervisors including IMS Fellows, RSS award recipients, and researchers with grants from NSF, EPSRC, and ERC.

02

Fully Funded Positions

Competitive stipends, conference travel funding, and tuition waivers for all successful PhD candidates.

03

Collaborative Clusters

Four research clusters create a vibrant intellectual community through seminars, reading groups, and joint publications.

04

Global Placement Record

PhD graduates placed at Stanford, Oxford, Google DeepMind, Bank of England, WHO, and leading global universities.

Apply for PhD Statistics →

Supervisor matching available — contact our graduate admissions team today