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This module aims to provide you with foundation of knowledge in the areas of health and well-being in later life and the impact of inequalities. You will be introduced to key issues and literature concerning the nature of ageing, quality of life, and well-being and how it is defined, and the structure and workings of healthcare systems and policies relating to health and social care of older people. Indicative topics include the debates and literature on inequalities in health; the concepts of informal care and frailty, and policies relating to older people. This module will critically examine the relative importance of different factors in health and the quality of life of older people and how policy and lifestyle choices and behaviours can influence these.

This module aims to provide you with foundation of knowledge in the areas of health and well-being in later life and the impact of inequalities. You will be introduced to key issues and literature concerning the nature of ageing, quality of life, and well-being and how it is defined, and the structure and workings of healthcare systems and policies relating to health and social care of older people. Indicative topics include the debates and literature on inequalities in health; the concepts of informal care and frailty, and policies relating to older people. This module will critically examine the relative importance of different factors in health and the quality of life of older people and how policy and lifestyle choices and behaviours can influence these.

This module explores how Artificial Intelligence (AI) is reshaping work, labour markets and the modern workplace. It will examine the effects of AI on job roles, organisational structures, skill requirements, and innovation.

This module is concerned with the development of modern societies and the nature of 'modernity'. It will draw on the writings of contemporary sociologists in order to consider what the most important processes of social change taking place are and how these have come about.

The goal of this module is to put into practise the skills and knowledge learned over the previous three semesters, and develop an original solution to a complex problem. Students will develop group working skills, and use appropriate engineering methods to identify requirements, design a solution, and manage the delivery and test of the solution. An example of the type of project that could be tackled would be the development of an intelligent robot system, integrating aspects of hardware design, computer engineering, software, artificial intelligence, and signal processing.

Examples of the types of skills applied from other modules might include:
- Designing custom PCBs to integrate sensor and compute modules
- Writing embedded C code and assembly to interface algorithms and peripherals
- Creating custom digital IP blocks to accelerate computationally intensive functions
- Creating SoCs with multiple CPUs and IP cores to meet sensor, processing and actuator requirements
- Modelling and implementing control loops for managing system functions
- Selecting and customising learning algorithms to perform specific tasks
- Integrating low-level control loops and high-level planning to achieve goals
- Designing and implementing signal-processing pipelines in software and hardware
- Optimising software algorithms and implementations to meet performance and power constraints
- Managing on-device storage of training and classification data
- Using analysis and filtering to pre-process and combine data streams into features

The project is introduced in the first semester to run alongside systematic design, so that students have a concrete example to think about in that module. This also gives students time to think about the project from a design point of view, without immediately moving to implementation. The bulk of the hours will be spent in the second semester, where the project development, integration, and testing will occur.

The goal of this module is to put into practise the skills and knowledge learned over the previous three semesters, and develop an original solution to a complex problem. Students will develop group working skills, and use appropriate engineering methods to identify requirements, design a solution, and manage the delivery and test of the solution. An example of the type of project that could be tackled would be the development of an intelligent robot system, integrating aspects of hardware design, computer engineering, software, artificial intelligence, and signal processing.

Examples of the types of skills applied from other modules might include:
- Designing custom PCBs to integrate sensor and compute modules
- Writing embedded C code and assembly to interface algorithms and peripherals
- Creating custom digital IP blocks to accelerate computationally intensive functions
- Creating SoCs with multiple CPUs and IP cores to meet sensor, processing and actuator requirements
- Modelling and implementing control loops for managing system functions
- Selecting and customising learning algorithms to perform specific tasks
- Integrating low-level control loops and high-level planning to achieve goals
- Designing and implementing signal-processing pipelines in software and hardware
- Optimising software algorithms and implementations to meet performance and power constraints
- Managing on-device storage of training and classification data
- Using analysis and filtering to pre-process and combine data streams into features

The project is introduced in the first semester to run alongside systematic design, so that students have a concrete example to think about in that module. This also gives students time to think about the project from a design point of view, without immediately moving to implementation. The bulk of the hours will be spent in the second semester, where the project development, integration, and testing will occur.

This thought-provoking module introduces you to the surveillance and predictive AI technologies informing policy and practice in contemporary. criminal justice systems. Theory and research from the fields of criminology, criminal justice studies, sociology, law, and the interdisciplinary STS scholarship are used to explore the prospects, challenges, and governance of the technologies. To deepen and broaden your knowledge of the key issues, international comparisons are embedded throughout. Participation in this module does not require prior technical knowledge of AI design processes. The module focuses on their implementation, challenges, and governance. It is as such suitable for postgraduates from diverse professional and academic backgrounds.

This thought-provoking module introduces you to the surveillance and predictive AI technologies informing policy and practice in contemporary. criminal justice systems. Theory and research from the fields of criminology, criminal justice studies, sociology, law, and the interdisciplinary STS scholarship are used to explore the prospects, challenges, and governance of the technologies. To deepen and broaden your knowledge of the key issues, international comparisons are embedded throughout. Participation in this module does not require prior technical knowledge of AI design processes. The module focuses on their implementation, challenges, and governance. It is as such suitable for postgraduates from diverse professional and academic backgrounds.

In this module you will examine the socio-technical influence of Artificial Intelligence (AI) on governance practices and global economic systems. You will explore how AI shapes decision-making processes, labour markets, international trade, public policy, ethics, and security, while also highlighting how these in turn affect the data, intellectual resources and human engagement on which the success of AI depends. The module also explores the challenges related to trust, regulation, inequality, and global cooperation. The course will address both the theoretical and practical aspects of AI integration into governance and economic structures.

In this module you will examine the socio-technical influence of Artificial Intelligence (AI) on governance practices and global economic systems. You will explore how AI shapes decision-making processes, labour markets, international trade, public policy, ethics, and security, while also highlighting how these in turn affect the data, intellectual resources and human engagement on which the success of AI depends. The module also explores the challenges related to trust, regulation, inequality, and global cooperation. The course will address both the theoretical and practical aspects of AI integration into governance and economic structures.