Module finder

This module aims to provide you with a comprehensive understanding of the fixed income securities and the analytical tools required for evaluating fixed income securities investments and strategies. Together with an understanding of investment data and performance measurement. You will develop core skills in bond valuation, yield curve analysis, credit risk assessment, and interest-rate risk management, and apply these in the context of modern portfolio construction. Reflecting the evolution of global capital markets, the module integrates the study of investment strategies and introduces sustainable fixed income instruments, such as green and sustainability-linked bonds. In addition, the module offers a brief session on the use of AI, machine learning, and fintech in bond analytics, providing you with awareness of emerging industry practices. The module blends financial theory, empirical evidence, and practical techniques for analysing fixed income markets and formulating investment strategies in the current financial environment.

This module aims to provide you with a thorough knowledge of the fixed income securities and techniques available for fixed income securities analysis, together with an understanding of investment data and performance measurement. Emphasis will be placed on the use of this knowledge in the current financial market environment.

This module will introduce and develop flexible statistical modelling methods that allow for general and complex forms of data to be modelled, extending ideas already encountered in earlier modules on linear and/or generalised linear modelling. The two main foci of the syllabus will be methods for modelling grouped data using random effects, and non-parametric “smoothing” methods for modelling data with complex functional form.

This module will introduce and develop flexible statistical modelling methods that allow for general and complex forms of data to be modelled, extending ideas already encountered in earlier modules on linear and/or generalised linear modelling. The two main foci of the syllabus will be methods for modelling grouped data using random effects, and non-parametric “smoothing” methods for modelling data with complex functional form.

This module will introduce and develop flexible statistical modelling methods that allow for general and complex forms of data to be modelled, extending ideas already encountered in earlier modules on linear and/or generalised linear modelling. The two main foci of the syllabus will be methods for modelling grouped data using random effects, and non-parametric “smoothing” methods for modelling data with complex functional form.

This module will introduce and develop flexible statistical modelling methods that allow for general and complex forms of data to be modelled, extending ideas already encountered in earlier modules on linear and/or generalised linear modelling. The two main foci of the syllabus will be methods for modelling grouped data using random effects, and non-parametric “smoothing” methods for modelling data with complex functional form.

This module will introduce and develop flexible statistical modelling methods that allow for general and complex forms of data to be modelled, extending ideas already encountered in earlier modules on linear and/or generalised linear modelling. The two main foci of the syllabus will be methods for modelling grouped data using random effects, and non-parametric “smoothing” methods for modelling data with complex functional form.

This module will provide the essentials of modelling and understanding the dynamics of aerospace vehicles: equations of motion derived from first principles, sensing and actuation systems and their limitations, model verification, implications for guidance and control.

This module will provide the essentials of modelling and understanding the dynamics of aerospace vehicles: equations of motion derived from first principles, sensing and actuation systems and their limitations, model verification, implications for guidance and control.

Floods are amongst the most damaging and costly of all natural hazards. Worldwide, frequent occurrences of heavy rainfall and other drivers combine with high levels of human exposure and high-value and vulnerable assets to produce multi-billion losses every year. Considering the world’s rapid urbanization, as well as the prospect of strongly adverse climate change effects, understanding and developing methods to mitigate the impacts of floods is attracting widespread concern and has become one of the top challenges of our generation. Crucial to our capacity to engineer rivers, cities and infrastructure that are resilient to floods is our ability to predict the probability or certain events (rainfall, storm surge, waves) to occur, and to model the corresponding process of inundation. The latter is used to accurately predict flow depths and velocities that will occur under different scenarios of rain or other flood-inducing factors and for existing or designed conditions. These models are extremely powerful tools that are used by engineers to optimise costly investments in flood risk mitigation systems, to support emergency relief measures, to price insurance premiums or to design flood-resilient infrastructure. With increasing demand for accurate predictions of flooding, it is important that engineers develop detailed understanding of how these tools can be used to predict and mitigate the risk of flooding.
This module will provide students with the knowledge required to use state-of-the-art models, and critically assess the results of flood simulations. By the end of the module students will also be able to judge and decide which, among the many models currently available, is best suited to simulate particular types of problems in engineering.