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This thirty credit module introduces the theoretical, ethical, logistical, technical and legislative issues involved in applying archaeological methods to underwater and coastal environments. It provides a broad grounding in the principles of maritime fieldwork, from survey design and project planning through to data acquisition, interpretation and reporting.
Through case studies and applied instruction, students examine how archaeological strategies are developed for submerged and intertidal contexts. The module introduces terrestrial, coastal and marine survey approaches, including geospatial survey techniques and the principles of marine geophysical prospection, alongside practical training in site recording and documentation.
Structured practical sessions and field-based exercises develop professional competencies in search, survey, recording and data management, including practical digital skills for data capture, visualisation and analysis. Non-divers participate on an equal footing through complementary roles in recording, survey coordination and project supervision. The module underpins practical training and fieldwork and complements the thematic perspectives explored in the Semester 1 core unit, Maritime Aspects of Culture.

Assessment is composed of two linked components: a group presentation developing a survey plan and methodology, followed by an individual Written Scheme of Investigation (final report) analysing and interpreting survey outcomes in relation to the research questions and objectives.

This fifteen credit module will introduce you to the theoretical, ethical, logistic, technical and legislative issues that have to be addressed if the theory and practice of archaeology are to be successfully applied in the investigation of sites underwater and/or in the coastal zone. Case studies will be used to demonstrate the logistical aspects of archaeological strategy, as well as the equipment and techniques necessary for search, survey, excavation and recording underwater and/or in the inter-tidal/coastal zone. The course includes practical sessions on survey and site recording. Non divers can participate on an equal footing to divers through alternative or associated activities related to recording and project supervision. This module is designed to underpin practical training and fieldwork, thereby complementing the more thematic approach explored in the first Semester Core Unit: ‘Maritime Aspects of Culture’. Assessment involves completing a portfolio of work that reflects the current requirements of applied maritime archaeology work in a professional context.

Behind many of today's engineering technologies—from finite element analysis and computer simulations to machine learning and robotics—lies the mathematics of linear algebra. This module develops a deep understanding of matrices and vector spaces, providing the mathematical tools that underpin modern computational engineering and data-driven technologies.

You will explore concepts including matrix operations, systems of equations, vector spaces, eigenvalues, eigenvectors and singular value decomposition, developing both intuitive understanding and mathematical confidence. Throughout the module, you will apply these ideas to a wide range of engineering problems, including structural analysis, numerical methods, optimisation, data analysis, machine learning and dynamic systems. Rather than studying mathematics in isolation, the emphasis is placed on understanding how these techniques enable engineers to solve real-world problems.

By the end of the module, you will have developed a powerful mathematical toolkit that supports advanced engineering analysis, computational modelling, artificial intelligence and many other areas of modern engineering practice.

A module focussed on properly understanding linear-algebra/matrices, which gives a lot of insight into how computation works, gives great perspective into many problems in mechanics, gives the language to describe machine learning, and has many other benefits. Matrices are used everywhere. The course will cover matrix inversion, algorithms for Ax=b, vector spaces, projection, properties of determinants, eigenvalues and eigenvectors, symmetric matrices, singular value decomposition. As we go through these general ideas, we will consider specific applications such as finite difference method for partial differential equations, linear regression, principal component analysis, trusses, numerical differentiation and integration, systems of ordinary differential equations, linear programming, neural networks, and others. Fundamentally, this is a mathematics course, but it is strongly focussed on intuitive understanding and applying the mathematics to different engineering problems.

Behind many of today's engineering technologies—from finite element analysis and computer simulations to machine learning and robotics—lies the mathematics of linear algebra. This module develops a deep understanding of matrices and vector spaces, providing the mathematical tools that underpin modern computational engineering and data-driven technologies.

You will explore concepts including matrix operations, systems of equations, vector spaces, eigenvalues, eigenvectors and singular value decomposition, developing both intuitive understanding and mathematical confidence. Throughout the module, you will apply these ideas to a wide range of engineering problems, including structural analysis, numerical methods, optimisation, data analysis, machine learning and dynamic systems. Rather than studying mathematics in isolation, the emphasis is placed on understanding how these techniques enable engineers to solve real-world problems.

By the end of the module, you will have developed a powerful mathematical toolkit that supports advanced engineering analysis, computational modelling, artificial intelligence and many other areas of modern engineering practice.

Behind many of today's engineering technologies—from finite element analysis and computer simulations to machine learning and robotics—lies the mathematics of linear algebra. This module develops a deep understanding of matrices and vector spaces, providing the mathematical tools that underpin modern computational engineering and data-driven technologies.

You will explore concepts including matrix operations, systems of equations, vector spaces, eigenvalues, eigenvectors and singular value decomposition, developing both intuitive understanding and mathematical confidence. Throughout the module, you will apply these ideas to a wide range of engineering problems, including structural analysis, numerical methods, optimisation, data analysis, machine learning and dynamic systems. Rather than studying mathematics in isolation, the emphasis is placed on understanding how these techniques enable engineers to solve real-world problems.

By the end of the module, you will have developed a powerful mathematical toolkit that supports advanced engineering analysis, computational modelling, artificial intelligence and many other areas of modern engineering practice.

This module introduces students to how mechatronic systems behave and how they can be controlled, using practical examples that gradually increase in complexity. You will begin by studying simple mechanical systems driven by electric motors and hydraulic actuators, learning how to describe their motion and understand their dynamic behaviour. Building on this foundation, the module explores systems with more complex motion, multiple interacting parts, and nonlinear effects.
Throughout the module, you will gain hands on experience using simulation tools and laboratory equipment to model systems, test control ideas, and see how theory links to real hardware. By the end, you will have developed practical skills in analysing and controlling mechatronic systems relevant to modern robotics and automation.

This module covers the application of concepts and methods in contemporary microeconometrics to address various applied research questions using mainly individual level (e.g. firms, households) micro data. Many of the examples will come from labour, public and financial economics, but the techniques covered in this course are applicable to a wide range of fields.

This module covers the application of concepts and methods in contemporary microeconometrics to address various applied research questions using mainly individual level (e.g. firms, households) micro data. Many of the examples will come from labour, public and financial economics, but the techniques covered in this course are applicable to a wide range of fields.

This module covers the application of concepts and methods in contemporary microeconometrics to address various applied research questions using mainly individual level (e.g. firms, households) micro data. Many of the examples will come from labour, public and financial economics, but the techniques covered in this course are applicable to a wide range of fields.