This module aims to develop the study, employability and research skills to meet the complex learning and professional requirements of postgraduate study. The module which is delivered through a blended approach brings together three discrete elements of study, employability and research skills. Learning content will be delivered through a combination of synchronous and asynchronous teaching provision.

Key aims include:

• To deliver a series of high quality, interactive study skills and blended learning activities to provide students with a broad foundation to support their development within their chosen field.
• To provide a series of skills to support students within their employability profiles.
• To provide students with the opportunity to develop research skills for engineering and business, including data gathering and analysis skills and ethical awareness.
• To provide students with the opportunity to develop effective communication skills for engineering and business, including skills synthesise complex scientific data to engineering and non-engineering audiences.

The aims of this module are:

• To give students an opportunity to conduct a research and/or development project on a topic of relevance to their specific programme of study.
• To provide students with the key skills and experience needed to plan, manage and deliver a complex extended project.
• To prepare students for future employment and professional practice in a relevant engineering sector at an advanced technical or managerial level.

This module aims to equip students with a robust understanding and hands-on experience of how digital and data engineering principles can transform engineering processes across the entire systems engineering lifecycle - from requirements and concept through design, development, verification, operation, and end-of-life.

Students will develop an appreciation of the organisational, cultural, and strategic dimensions of digital transformation, understanding how robust governance and data-driven practices underpin responsible engineering outcomes, particularly in data-intensive systems.

The module is designed to equip engineering managers with the core competencies for delivering technical engineering projects.

The core aim of this module is to ensure students are able to take advantage of Machine Learning (ML) techniques to solve practical engineering problems. The module aims to:

• Provide a base understanding of Machine Learning (ML) in the wider context of Artificial Intelligence (AI).
• Establish ML approaches and algorithms.
• Provide development tools to deploy ML applications.
• Explore ML techniques in practical engineering contexts.
• Establish the challenges with ML in engineering.

The aims of this module is to introduce students from mechanical and manufacturing engineering backgrounds to both the opportunities and constraints of the manufacturing practice when using a biological production system or input material. The module focuses on clinically used products and their applicable regulations, manufacturing process design and quality control, with particular reference to tissue engineering and regenerative medicine applications.