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The course curriculum of the Management Science and Operations PhD.

The doctoral programme in Management Science and Operations (MSO) involves a comprehensive and rigorous curriculum of graduate-level courses providing students with deep methodological training. This includes core PhD courses, advanced MSO courses and concentration courses, as follows. 

Core courses

  • Foundations of Business Research

    Introduction to the logic, history and philosophy of research in the social sciences. Provides a multi-disciplinary perspective on key methodological concepts related to the knowledge creation process and the credibility of research in business studies.

  • Statistical Research Methods

    Overview of empirical research techniques for econometric modelling and multivariate data analysis. Emphasis on techniques establishing causality, including instrumental variables, fixed effects regression, natural experiments, propensity score matching and event studies. Also covers experimental design topics (moderation / mediation, validity, factorial analysis, power and effect size).

  • Econometrics

    Rigorous introduction to the micro-econometric tools used in empirical economic research, with a focus on applications (assumptions required, proper execution, results interpretation).

  • Microeconomics

    Doctoral-level introduction to microeconomic theory oriented towards research applications. Covers static and dynamic games of complete and incomplete information, moral hazard, the principal/agent model and contracting.

  • Mathematical Programming: Theory and Algorithms

    Rigorous introduction to linear and nonlinear programming problems covering duality theory, the simplex method, Lagrangian duality, convex programming and KKT conditions, algorithms for linear and convex optimisation problems, the theory of good integer programming formulations and integer programming algorithms.

  • Stochastic Processes

    Introduction to key ideas and methods in applied probability, together with application examples. Covers Markov chains in discrete and continuous time (random walks, branching processes, reversibility, embedded chains, simulation and estimation, forward and backward decompositions, reversibility arguments); point processes in time and space (complete and conditional intensities, superposition and thinning, Poisson-based processes, Markov random fields); and epidemic models (thresholds, deterministic and moment closure approximations, population structure, host heterogeneity and heterogeneity of mixing, epidemics on networks).

Advanced MSO courses

  • Advanced Optimization

    Covers convex/nonlinear optimization and optimization under uncertainty, as well as other special topics reflecting the latest research in optimization.

  • Dynamic Programming

    Introduction to quantitative decision making under uncertainty through Dynamic Programming. Covers basic mathematical formulations and solution concepts for important applications (inventory management, asset selling, portfolio selection), and methodological extensions relevant to data science such as problems with imperfect state information and computational / approximate DP techniques.

  • Queueing Theory

    Covers advanced stochastic models with a special emphasis on queueing systems and dynamic control problems. Topics include arrival processes, basic laws governing queueing systems, Markovian queues, the M/G/1 queue, and Jackson queueing networks.

  • Revenue Management

    Provides in-depth coverage of the theory and current research in Dynamic Pricing and Revenue Management. Also covers a breadth of applications, with an emphasis on operational models, their estimation and solutions.

  • Professional Seminar

    Interactive seminar dedicated to professional issues relevant to careers in academia, including (but not limited to) research styles, modelling choices, relationships with industry, paper writing, the publication process and the academic job market.

MSO PhD Concentration


The concentration is composed of three advanced graduate-level courses forming a coherent area of study with a heavy methodological focus. Concentration courses are entirely determined by each student according to individual interests. Examples of possible concentrations include:

  • Optimisation concentration: Combinatorial Optimisation, Convex Optimisation, Continuous Optimisation
  • Stochastic modelling concentration: Measure-Theoretic Probability, Advanced Stochastic Processes, Stochastic Networks
  • Micro-economics concentration: Advanced Econometrics, Advanced Game Theory, Contract Theory
  • Financial engineering concentration: Finance Theory, Stochastic Calculus, Convex Optimisation


A unique programme feature is the ability of our students to follow courses taught by recognized scholars from other universities of the greater London area such as the University of Cambridge, Oxford University, University College London, Imperial College and the London School of Economics.




Next steps

Apply for the MSO PhD Programme: