Imperial College London

This course examines kinematics and kinetics of human locomotion, bone, and soft tissue failure, macro- and micro-circulatory mechanics in various organs, and practical approaches to quantifying biomechanics. It describes how mechanics plays a role in basic physiological processes in the human body, as well as employing kinematic and kinetic principles to describe human locomotion. The course explores failure mechanisms of bone, as well as the differences between macro and microcirculatory flows. Students examine mass and fluid transport mechanisms in physiology.

This course explores debates around collective intelligence and follow the evolution of the group-mind from past to present and into the future. It looks at how the act of thinking together can go wrong in paranoid conspiracy theories, information bubbles and market panics and how, perhaps, it might be done better.

In this course, students complete a long-term individual project in order to demonstrate independence and originality, to plan and organize a large project over a long period, and to put into practice knowledge, skills, and research methods. Students are able to submit an original proposal, or browse from projects proposed by prospective supervisors.

This course guides students through the “exciting nightmare” of taking an idea or a technology to market, growing the venture, and securing a successful exit. Although grounded in rigorous theory, the focus of the course is highly practical and class participation is actively encouraged. No prior knowledge of the subject is required but students should be interested in the creation of wealth and the commercialization of technology. 

This course introduces students to the core ideas and fundamental concepts behind machine learning. Students learn different machine learning problems and the algorithms that exist to address them. They formulate machine learning problems and machine learning pipelines, apply suitable algorithms to tackle different machine learning tasks, implement machine learning algorithms to solve supervised learning problems, and assess appropriate methodologies to evaluate machine learning algorithms.

This course explores industrial practices for working on large, existing, software systems. Students discuss how to successfully design, modify, maintain, and operate the large software systems that form so much of the infrastructure of trade, commerce, communication, and entertainment in the modern world. Students also consider current issues faced by the practicing software engineer, and particularly look at engineering trade-offs in different situations and understand that software engineering problems do not always have right and wrong answers.

This course teaches students to consider the challenges posed by climate change, and the technologies and systems that are required to mitigate it. Students are introduced to key mitigation technologies and given the skills to perform basic economic analysis of the options. Lectures cover technoeconomic assessment and emissions estimation methods, possible future technology developments, and approaches to systems thinking, as well as the policy background on climate change. 

This class consists of an extended laboratory, over 10 weeks, to investigate how a particular processing parameter influences the structure, properties and performance of a material. Each group of students is asked to determine the processing parameters that optimizes the performance of a material for a particular application. Students design and perform a systematic series of experiments to meet the objective, each student produces an individual report on the investigation. 

The course develops the tools required for the application of new energy and renewable energy systems to the problems faced by climate change and global energy security while transitioning to a zero emissions economy. The focus is on the application of materials for the development of new energy recovery systems such as nanostructured surfaces for solar harvesting, solar fuels, batteries/capacitors, and fuel cells/electrolysers. Biomass as a potential alternative to clean energy is also discussed along with its different scenarios and the associated advantages and risks. 

The course teaches students a thorough understanding of high-performance and energy-efficient computer architecture. Students learn principles and techniques for evaluating architectural proposals, explore how knowledge of computer architecture informs software performance engineering, and gain a deep understanding of topical trends in advanced computer architecture, compiler design, operating systems, and parallel processing