8546 modules
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OPTO6007 2025-26
Silicon Photonics
The course will present an introduction to guided waves, optical modes, and propagation characteristics of photonic circuits, using Silicon Technology by way of example. -
OPTO6007 2026-27
Silicon Photonics
The course will present an introduction to guided waves, optical modes, and propagation characteristics of photonic circuits, using Silicon Technology by way of example. -
RESM1002 2025-26
Simple Liars, Damned Liars and Experts: the use of empirical research in social science
A key skill of a social scientist is to be able to assess the quality of evidence presented based on strong methodological foundations. We need to understand what constitutes evidence, including how it can be produced, agreed, disputed, disseminated and misrepresented. In the era of sharing ‘facts’ on social media and ‘fake news’ it is vital that an appreciation of how evidence and expert opinion is developed. Everybody can seem like an expert on social media, but the ability to examine and evaluate the statements made is a skill that can be developed. Expertise is learnt through a grounding in evidence and understanding which evidence is sound and which is not. All social science researchers need the ability to judge the quality of research, both for academic research and in the wider world.
The module will follow on from the Semester 1 module ‘Understanding the Social World’ and explore issues around the generation and presentation of evidence. This will include assessing the quality of social science data, as well as further exploration of both qualitative and quantitative methods to obtain and analyse data. This module forms the basis to move to more detailed methodological training in future years of study. -
RESM1002 2026-27
Simple Liars, Damned Liars and Experts: the use of empirical research in social science
A key skill of a social scientist is to be able to assess the quality of evidence presented based on strong methodological foundations. We need to understand what constitutes evidence, including how it can be produced, agreed, disputed, disseminated and misrepresented. In the era of sharing ‘facts’ on social media and ‘fake news’ it is vital that an appreciation of how evidence and expert opinion is developed. Everybody can seem like an expert on social media, but the ability to examine and evaluate the statements made is a skill that can be developed. Expertise is learnt through a grounding in evidence and understanding which evidence is sound and which is not. All social science researchers need the ability to judge the quality of research, both for academic research and in the wider world.
The module will follow on from the Semester 1 module ‘Understanding the Social World’ and explore issues around the generation and presentation of evidence. This will include assessing the quality of social science data, as well as further exploration of both qualitative and quantitative methods to obtain and analyse data. This module forms the basis to move to more detailed methodological training in future years of study. -
MANG6122 2026-27
Simulation
This module provides a practical introduction to the theories and techniques of simulation. The approach taken is very broad and covers different forms of simulation, including discrete event simulation, system dynamics and agent-based modelling. The module focuses on practical applications of simulations in a variety of contexts, and students will gain expertise in simulation software. -
MANG6122 2025-26
Simulation
This module provides a practical introduction to the theories and techniques of simulation. The approach taken is very broad and covers different forms of simulation, including discrete event simulation, system dynamics and agent-based modelling. The module focuses on practical applications of simulations in a variety of contexts, and students will gain expertise in simulation software. -
ELEC2302 2027-28
Simulation and Modelling for Electrical Engineering
This module introduces some advanced programming, simulation and design modelling frameworks and tools. Teaching activities are a combination of taught sessions, expanded self-study supported by the Professional Skills Hub and practical hands-on sessions in computer laboratories. The tools and techniques studied in this module are also used in the companion design module in practical hands on applications. -
ELEC2302 2026-27
Simulation and Modelling for Electrical Engineering
This module introduces some advanced programming, simulation and design modelling frameworks and tools. Teaching activities are a combination of taught sessions, expanded self-study supported by the Professional Skills Hub and practical hands-on sessions in computer laboratories. The tools and techniques studied in this module are also used in the companion design module in practical hands on applications. -
CHEM3058 2028-29
Simulation Methods in Chemistry
This module builds on the student’s core understanding of the structure of atoms and molecules to predict their behaviour using state-of-the art computational chemistry methods. This will involve learning how quantum chemistry methods can be used to study atoms and molecules and how classical mechanics methods can be used to simulate molecules and biomolecules. These two methodologies are related and we will explore their respective and mutual applications. Emphasis will be placed upon learning how to use these methods for real-life applications. -
COMP6216 2026-27
Simulation Modelling for Computer Science
Simulation modelling plays an increasingly significant role across modern science and engineering, with the development of computational models becoming established practice in industry, consulting, and policy formulation. Computer scientists are often employed as modellers or software engineers to help in the model development & maintenance cycle. Therefore this is a current and future need for computer science graduates to have a grounding in both the philosophy of modelling in science and various modelling techniques.
This module will familiarise students with general knowledge about the role of modelling in science (with a particular emphasis on computational modelling), will discuss the process of model development and best practice in various stages in the model development cycle. A second (and larger) part of the module will provide a broad survey of the central modelling paradigms.
Throughout the module we will demonstrate how computer science techniques are used to develop models in the following domains:
- Information networks
- Design and management of infrastructure
- Epidemics
- Natural resource management
- Computational economics
- Collective robotics
- Online trading systems
- Climate and Earth system processes