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Computational methods play an ever increasing role for the successful development of cost-effective and robust engineering solutions to address the challenges emerging from a healthcare agenda calling for prolonging independent living and the personalisation/stratification of care in our ageing societies.

The module will provide the theoretical basis and practical training in fundamental engineering skills required to develop innovative and robust design solutions for a range of technologies such as surgical tools, instrumentation, artificial joints, stents, minimally invasive surgery, and assistive technology including devices for rehabilitation and independent living. The module will introduce some of the key theories and computational methods that capture essential aspects of patient variability in predictive numerical tools and enable the development of robust technology for prevention, diagnosis, treatment and rehabilitation.

Demonstration of the use of the computational methods will concentrate on orthopaedic applications and more specifically on the analysis of the biomechanical behaviour of the musculoskeletal system. Here, key concepts and approaches with which the students will be familiarized with include methods for the reconstruction of 3D musculoskeletal anatomy from medical image data, the recording and description of skeletal kinematics as well as state of the art approaches for the calculation of muscle and joint forces. In a further step, these techniques will provide input to advanced numerical modelling techniques to predict and optimize the performance of joint replacements and the course of bone healing after a fracture.

The presentation and discussion of further case studies on cardiovascular applications will enable the students to understand how such computational tools can be successfully applied to a broad range of biomedical engineering design problems.

Modern statistics relies on computational methods for most practical applications. This module provides an introduction to computer intensive methods and their application to common modelling and inference problems. The focus is on introducing methodology and algorithms, implemented in the R programming language.

Modern statistics relies on computational methods for most practical applications. This module provides an introduction to computer intensive methods and their application to common modelling and inference problems. The focus is on introducing methodology and algorithms, implemented in the R programming language.

This module aims to give students an understanding of how a CPU works, and also the ability to implement a working CPU. The module covers basic data- and control-path design, and the implemention of an existing Instruction Set Architecture (ISA). Standard optimisations (pipelining and caches) are introduced to explain basic techniques for improving performance. The module shows how a CPU can be used as one component within a larger computational system, for example how CPUs are integrated with other devices within a modern System on Chip (SoC).

This module aims to give students an understanding of how a CPU works, and also the ability to implement a working CPU. The module covers basic data- and control-path design, and the implemention of an existing Instruction Set Architecture (ISA). Standard optimisations (pipelining and caches) are introduced to explain basic techniques for improving performance. The module shows how a CPU can be used as one component within a larger computational system, for example how CPUs are integrated with other devices within a modern System on Chip (SoC).

This module aims to give students an understanding of the fundamentals of computer hardware and of the principles of operation of computers and peripheral devices. In addition, the module aims to give an overview of the main families of microprocessors and their differences. Some digital electronics is also covered - with hands-on experience in the lab with a Raspberry Pi in order to better understand computer fundamentals.

This module aims to give students an understanding of the fundamentals of computer hardware and of the principles of operation of computers and peripheral devices. In addition, the module aims to give an overview of the main families of microprocessors and their differences. Some digital electronics is also covered - with hands-on experience in the lab with a Raspberry Pi in order to better understand computer fundamentals.