This module uses multiple tools of design and innovation and explores the utilisation of such tools in several contexts including human-centred design, sustainability, and inclusivity in design with an emphasis on practical solutions.

This module aims to lay the foundation for taking an innovative solution through a strategy development process and assessing opportunities and potential threats prior to positioning a venture within a competitive or uncontested market.

In this module, you will conduct a substantial solo Design Engineering project, representing the culmination of your journey over the past two terms. You will use your acquired knowledge, skills and attitude to bring to fruition a deep-dive project.

This module introduces design engineering approaches to resolve complex engineering challenges and will equip you with methods and tools to develop innovative solutions, i.e. a systematic approach used to reach the desired solution to a problem.

This module will teach you the basics of transdisciplinary mixed methods research (tMM), which includes objective and subjective assessments of problems at hand and bridges domains of natural/biomedical sciences and the social/human sciences.

This module will enable you to gain an advanced understanding and apply state-of-the-art transdisciplinary mixed methods (tMM) approaches to solving complex problems, including multimodality and data triangulation.

This module provides you with basic tools to consider human behaviour and experience to affect impactful design solutions to global challenges.

Collaborative consumption, or the sharing economy as it is also known, refers to businesses that are based on sharing resources and services, as opposed to traditional ownership-based models.

This module intends to introduce students to the fundamentals of DLTs rigorously. The content will be continuously motivated by the social requirements and contexts of the problems they seek to solve.

The module focuses on various aspects of data science to design products from data. It first defines and introduces data products, as well as alternative ways to design with data.

This module will deliver a comprehensive introduction to Nanotechnology for Design Engineers. This course commences with an introduction to how the fundamental physical, electrical and optical properties of materials change on the nanoscale compared to their bulk counterparts.

This module aims to introduce you to critical ways of thinking about technology products and their impact on individuals, their health and society as a whole. The module explores design techniques for understanding stakeholders' values, and ethical and responsible innovation frameworks.

Robotics Research Project will provide an overview of the latest research in the field of applied robotics, as well as a hands-on approach to bring critical skills together. This is done in a project-oriented course where students will design mechanical, electrical, and software subsystems of an overall functioning robot.

The module aims to provide students with sufficient tools and techniques to develop software and hardware platforms for the Internet of Things, obtain data from mobile and social sensors, perform data analysis, perform actuations, and use key insights from data mining.

This module enables students to understand, critique, and create games and playful experiences for entertainment and social issues like health, education, sustainability, or social change.

This module will give a view of how numeric simulations can be applied to problems in Design Engineering (and some problems selected from physical, biological, and social sciences). An underpinning of numerical methods, algorithms, and analysis tools will complement applications to a range of systems, such as infection modelling and crowd dynamics.

This module provides engineering students with the platform needed to solve future industry challenges, get the most out of 3D printing technology and optimise designs.

This module aims to provide overall knowledge and understanding of the human visual system, and the technology available for design engineers to acquire, analyse, interpret and exploit visual information gathered with modern electronic components.

This module provides a strong emphasis on the application of commonly used inferential statistics approaches for social and behavioural sciences. You will learn about different modes of data and common tests and analyses (including, but not limited to, t-test, ANOVA, correlation, regression, and generalised linear modelling), as well as data cleaning and preparation techniques.

This module aims to provide an understanding of the tools and techniques available to implement sustainable design and provide knowledge of the methods a company can employ to reduce environmental impacts, promote sustainable practices and build resilient propositions.

TBC

This module provides the opportunity to further develop industrial design skills and knowledge to a high professional level alongside gaining an understanding of significant emerging themes and methodologies within the field.

This module is an introduction to audio technology and perception, which includes acoustics, psychoacoustics, digital signal processing, audio recording techniques, audio reproduction techniques, 3D sound synthesis and reproduction and other selected topics.

This module will develop your competence in scoping, analysing and developing creative interventions for behaviour change. Possible interventions include wearables to improve health, products for more sustainable consumption and policy to improve tech-enabled business models.

The module focuses on core principles in micro-economics, accounting, finance, uncertainty modelling, and decision-making needed to assess the economic performance of engineering systems and products.

This module aims at giving relevant basics to the students to analyse interaction within systems as games. It introduces the principles to describe agents and the type of games they are involved in.

The objective of this module is to de-mystify the topic and expose students, in an accessible manner, to both the basics of machine learning and to some of its most important methods.